{"cells":[{"cell_type":"markdown","metadata":{},"source":["## 第一期 Pandas基础"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import numpy as np"]},{"cell_type":"markdown","metadata":{},"source":["### 1.将下面的字典创建为DataFrame"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[],"source":["data = {\"grammer\":[\"Python\",\"C\",\"Java\",\"GO\",np.nan,\"SQL\",\"PHP\",\"Python\"],\n","       \"score\":[1,2,np.nan,4,5,6,7,10]}"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>grammer</th>\n","      <th>score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Python</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>C</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Java</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>GO</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NaN</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>SQL</td>\n","      <td>6.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>PHP</td>\n","      <td>7.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Python</td>\n","      <td>10.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  grammer  score\n","0  Python    1.0\n","1       C    2.0\n","2    Java    NaN\n","3      GO    4.0\n","4     NaN    5.0\n","5     SQL    6.0\n","6     PHP    7.0\n","7  Python   10.0"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.DataFrame(data)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 2.提取含有字符串\"Python\"的行"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>grammer</th>\n","      <th>score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Python</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Python</td>\n","      <td>10.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  grammer  score\n","0  Python    1.0\n","7  Python   10.0"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["#方法一\n","df[df['grammer'] == 'Python']\n","#方法二\n","results = df['grammer'].str.contains(\"Python\")\n","results.fillna(value=False,inplace = True)\n","df[results]"]},{"cell_type":"markdown","metadata":{},"source":["### 3.输出df的所有列名"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Index(['grammer', 'score'], dtype='object')\n"]}],"source":["print(df.columns)"]},{"cell_type":"markdown","metadata":{},"source":["### 4.修改第二列列名为'popularity'"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>grammer</th>\n","      <th>popularity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Python</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>C</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Java</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>GO</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NaN</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>SQL</td>\n","      <td>6.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>PHP</td>\n","      <td>7.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Python</td>\n","      <td>10.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  grammer  popularity\n","0  Python         1.0\n","1       C         2.0\n","2    Java         NaN\n","3      GO         4.0\n","4     NaN         5.0\n","5     SQL         6.0\n","6     PHP         7.0\n","7  Python        10.0"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.rename(columns={'score':'popularity'}, inplace = True)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 5.统计grammer列中每种编程语言出现的次数"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/plain":["Python    2\n","GO        1\n","Java      1\n","PHP       1\n","C         1\n","SQL       1\n","Name: grammer, dtype: int64"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df['grammer'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["### 6.将空值用上下值的平均值填充"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>grammer</th>\n","      <th>popularity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Python</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>C</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Java</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>GO</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NaN</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>SQL</td>\n","      <td>6.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>PHP</td>\n","      <td>7.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Python</td>\n","      <td>10.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  grammer  popularity\n","0  Python         1.0\n","1       C         2.0\n","2    Java         3.0\n","3      GO         4.0\n","4     NaN         5.0\n","5     SQL         6.0\n","6     PHP         7.0\n","7  Python        10.0"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df['popularity'] = df['popularity'].fillna(df['popularity'].interpolate())\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 7.提取popularity列中值大于3的行"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>grammer</th>\n","      <th>popularity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>GO</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NaN</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>SQL</td>\n","      <td>6.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>PHP</td>\n","      <td>7.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Python</td>\n","      <td>10.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  grammer  popularity\n","3      GO         4.0\n","4     NaN         5.0\n","5     SQL         6.0\n","6     PHP         7.0\n","7  Python        10.0"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df[df['popularity'] > 3]"]},{"cell_type":"markdown","metadata":{},"source":["### 8.按照grammer列进行去除重复值"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>grammer</th>\n","      <th>popularity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Python</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>C</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Java</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>GO</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NaN</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>SQL</td>\n","      <td>6.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>PHP</td>\n","      <td>7.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  grammer  popularity\n","0  Python         1.0\n","1       C         2.0\n","2    Java         3.0\n","3      GO         4.0\n","4     NaN         5.0\n","5     SQL         6.0\n","6     PHP         7.0"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df.drop_duplicates(['grammer'])"]},{"cell_type":"markdown","metadata":{},"source":["### 9.计算popularity列平均值"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/plain":["4.75"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df['popularity'].mean()"]},{"cell_type":"markdown","metadata":{},"source":["### 10.将grammer列转换为list"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/plain":["['Python', 'C', 'Java', 'GO', nan, 'SQL', 'PHP', 'Python']"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["df['grammer'].to_list()"]},{"cell_type":"markdown","metadata":{},"source":["### 11.将DataFrame保存为EXCEL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.to_excel('test.xlsx')"]},{"cell_type":"markdown","metadata":{},"source":["### 12.查看数据行列数"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"data":{"text/plain":["(8, 2)"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"markdown","metadata":{},"source":["### 13.提取popularity列值大于3小于7的行"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>grammer</th>\n","      <th>popularity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>GO</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NaN</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>SQL</td>\n","      <td>6.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  grammer  popularity\n","3      GO         4.0\n","4     NaN         5.0\n","5     SQL         6.0"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["df[(df['popularity'] > 3) & (df['popularity'] < 7)]"]},{"cell_type":"markdown","metadata":{},"source":["### 14.交换两列位置"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>popularity</th>\n","      <th>grammer</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>Python</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>Java</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>GO</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6.0</td>\n","      <td>SQL</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7.0</td>\n","      <td>PHP</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>10.0</td>\n","      <td>Python</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   popularity grammer\n","0         1.0  Python\n","1         2.0       C\n","2         3.0    Java\n","3         4.0      GO\n","4         5.0     NaN\n","5         6.0     SQL\n","6         7.0     PHP\n","7        10.0  Python"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["'''\n","方法1\n","'''\n","temp = df['popularity']\n","df.drop(labels=['popularity'], axis=1,inplace = True)\n","df.insert(0, 'popularity', temp)\n","df\n","'''\n","方法2\n","cols = df.columns[[1,0]]\n","df = df[cols]\n","df\n","'''"]},{"cell_type":"markdown","metadata":{},"source":["### 15.提取popularity列最大值所在行"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>popularity</th>\n","      <th>grammer</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>7</th>\n","      <td>10.0</td>\n","      <td>Python</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   popularity grammer\n","7        10.0  Python"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["df[df['popularity'] == df['popularity'].max()]"]},{"cell_type":"markdown","metadata":{},"source":["### 16.查看最后5行数据"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>popularity</th>\n","      <th>grammer</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>GO</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6.0</td>\n","      <td>SQL</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7.0</td>\n","      <td>PHP</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>10.0</td>\n","      <td>Python</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   popularity grammer\n","3         4.0      GO\n","4         5.0     NaN\n","5         6.0     SQL\n","6         7.0     PHP\n","7        10.0  Python"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["df.tail()"]},{"cell_type":"markdown","metadata":{},"source":["### 17.删除最后一行数据"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>popularity</th>\n","      <th>grammer</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>Python</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>Java</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>GO</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6.0</td>\n","      <td>SQL</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7.0</td>\n","      <td>PHP</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   popularity grammer\n","0         1.0  Python\n","1         2.0       C\n","2         3.0    Java\n","3         4.0      GO\n","4         5.0     NaN\n","5         6.0     SQL\n","6         7.0     PHP"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["df.drop([len(df)-1],inplace=True)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 18.添加一行数据['Perl',6.6] "]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>popularity</th>\n","      <th>grammer</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>Python</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>Java</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>GO</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6.0</td>\n","      <td>SQL</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7.0</td>\n","      <td>PHP</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>6.6</td>\n","      <td>Perl</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   popularity grammer\n","0         1.0  Python\n","1         2.0       C\n","2         3.0    Java\n","3         4.0      GO\n","4         5.0     NaN\n","5         6.0     SQL\n","6         7.0     PHP\n","7         6.6    Perl"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["row={'grammer':'Perl','popularity':6.6}\n","df = df.append(row,ignore_index=True)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 19.对数据按照\"popularity\"列值的大小进行排序"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>popularity</th>\n","      <th>grammer</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>Python</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>Java</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>GO</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6.0</td>\n","      <td>SQL</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>6.6</td>\n","      <td>Perl</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7.0</td>\n","      <td>PHP</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   popularity grammer\n","0         1.0  Python\n","1         2.0       C\n","2         3.0    Java\n","3         4.0      GO\n","4         5.0     NaN\n","5         6.0     SQL\n","7         6.6    Perl\n","6         7.0     PHP"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["df.sort_values(\"popularity\",inplace=True)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 20.统计grammer列每个字符串的长度"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[],"source":["df['grammer'] = df['grammer'].fillna('R')\n","df['len_str'] = df['grammer'].map(lambda x: len(x))\n","df"]},{"cell_type":"markdown","metadata":{},"source":["## 第二期 Pandas数据处理"]},{"cell_type":"markdown","metadata":{},"source":["### 21.读取本地EXCEL数据"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["import pandas as pd\n","df = pd.read_excel('pandas120.xlsx')"]},{"cell_type":"markdown","metadata":{},"source":["### 22.查看df数据前5行"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2020-03-16 11:30:18</td>\n","      <td>本科</td>\n","      <td>20k-35k</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2020-03-16 10:58:48</td>\n","      <td>本科</td>\n","      <td>20k-40k</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2020-03-16 10:46:39</td>\n","      <td>不限</td>\n","      <td>20k-35k</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2020-03-16 10:45:44</td>\n","      <td>本科</td>\n","      <td>13k-20k</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2020-03-16 10:20:41</td>\n","      <td>本科</td>\n","      <td>10k-20k</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           createTime education   salary\n","0 2020-03-16 11:30:18        本科  20k-35k\n","1 2020-03-16 10:58:48        本科  20k-40k\n","2 2020-03-16 10:46:39        不限  20k-35k\n","3 2020-03-16 10:45:44        本科  13k-20k\n","4 2020-03-16 10:20:41        本科  10k-20k"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","metadata":{},"source":["### 23.将salary列数据转换为最大值与最小值的平均值"]},{"cell_type":"code","execution_count":19,"metadata":{"scrolled":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n","  \"\"\"\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:961: FutureWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n","  return getattr(section, self.name)[new_key]\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: FutureWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n","  \n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2020-03-16 11:30:18</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2020-03-16 10:58:48</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2020-03-16 10:46:39</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2020-03-16 10:45:44</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2020-03-16 10:20:41</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>2020-03-16 11:36:07</td>\n","      <td>本科</td>\n","      <td>14000</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>2020-03-16 09:54:47</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>2020-03-16 10:48:32</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>2020-03-16 10:46:31</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>2020-03-16 11:19:38</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 3 columns</p>\n","</div>"],"text/plain":["             createTime education  salary\n","0   2020-03-16 11:30:18        本科   27500\n","1   2020-03-16 10:58:48        本科   30000\n","2   2020-03-16 10:46:39        不限   27500\n","3   2020-03-16 10:45:44        本科   16500\n","4   2020-03-16 10:20:41        本科   15000\n","..                  ...       ...     ...\n","130 2020-03-16 11:36:07        本科   14000\n","131 2020-03-16 09:54:47        硕士   37500\n","132 2020-03-16 10:48:32        本科   30000\n","133 2020-03-16 10:46:31        本科   19000\n","134 2020-03-16 11:19:38        本科   30000\n","\n","[135 rows x 3 columns]"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["#备注，在某些版本pandas中.ix方法可能失效，可使用.iloc，参考https://mp.weixin.qq.com/s/5xJ-VLaHCV9qX2AMNOLRtw\n","#为什么不能直接使用max，min函数，因为我们的数据中是20k-35k这种字符串，所以需要先用正则表达式提取数字\n","import re\n","# 方法一：apply + 自定义函数\n","def func(df):\n","    lst = df['salary'].split('-')\n","    smin = int(lst[0].strip('k'))\n","    smax = int(lst[1].strip('k'))\n","    df['salary'] = int((smin + smax) / 2 * 1000)\n","    return df\n","\n","df = df.apply(func,axis=1)\n","# 方法二：iterrows + 正则\n","import re\n","for index,row in df.iterrows():\n","    nums = re.findall('\\d+',row[2])\n","    df.iloc[index,2] = int(eval(f'({nums[0]} + {nums[1]}) / 2 * 1000'))"]},{"cell_type":"markdown","metadata":{},"source":["### 24.将数据根据学历进行分组并计算平均薪资"]},{"cell_type":"code","execution_count":40,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["                 salary\n","education              \n","不限         19600.000000\n","大专         10000.000000\n","本科         19361.344538\n","硕士         20642.857143\n"]}],"source":["print(df.groupby('education').mean())"]},{"cell_type":"markdown","metadata":{},"source":["### 25.将createTime列时间转换为月-日"]},{"cell_type":"code","execution_count":41,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n","  \n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  createTime education  salary\n","0      03-16        本科   27500\n","1      03-16        本科   30000\n","2      03-16        不限   27500\n","3      03-16        本科   16500\n","4      03-16        本科   15000"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["#备注，在某些版本pandas中.ix方法可能失效，可使用.iloc，参考https://mp.weixin.qq.com/s/5xJ-VLaHCV9qX2AMNOLRtw\n","for i in range(len(df)):\n","    df.ix[i,0] = df.ix[i,0].to_pydatetime().strftime(\"%m-%d\")  \n","df.head()"]},{"cell_type":"markdown","metadata":{},"source":["### 26.查看索引、数据类型和内存信息"]},{"cell_type":"code","execution_count":44,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 135 entries, 0 to 134\n","Data columns (total 4 columns):\n","createTime    135 non-null object\n","education     135 non-null object\n","salary        135 non-null int64\n","categories    135 non-null category\n","dtypes: category(1), int64(1), object(2)\n","memory usage: 3.5+ KB\n"]}],"source":["df.info()"]},{"cell_type":"markdown","metadata":{},"source":["### 27.查看数值型列的汇总统计"]},{"cell_type":"code","execution_count":42,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>salary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>135.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>19159.259259</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>8661.686922</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>3500.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>14000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>17500.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>25000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>45000.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             salary\n","count    135.000000\n","mean   19159.259259\n","std     8661.686922\n","min     3500.000000\n","25%    14000.000000\n","50%    17500.000000\n","75%    25000.000000\n","max    45000.000000"]},"execution_count":42,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["### 28.新增一列根据salary将数据分为三组"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>categories</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","      <td>中</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","      <td>中</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>14000</td>\n","      <td>中</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","      <td>中</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>高</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 4 columns</p>\n","</div>"],"text/plain":["    createTime education  salary categories\n","0        03-16        本科   27500          高\n","1        03-16        本科   30000          高\n","2        03-16        不限   27500          高\n","3        03-16        本科   16500          中\n","4        03-16        本科   15000          中\n","..         ...       ...     ...        ...\n","130      03-16        本科   14000          中\n","131      03-16        硕士   37500          高\n","132      03-16        本科   30000          高\n","133      03-16        本科   19000          中\n","134      03-16        本科   30000          高\n","\n","[135 rows x 4 columns]"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["bins = [0,5000, 20000, 50000]\n","group_names = ['低', '中', '高']\n","df['categories'] = pd.cut(df['salary'], bins, labels=group_names)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 29.按照salary列对数据降序排列"]},{"cell_type":"code","execution_count":45,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>categories</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>53</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>45000</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>40000</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>37500</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>37500</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>123</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>4500</td>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>126</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>4000</td>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>110</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>4000</td>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>96</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>3500</td>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>113</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>3500</td>\n","      <td>低</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 4 columns</p>\n","</div>"],"text/plain":["    createTime education  salary categories\n","53       03-16        本科   45000          高\n","37       03-16        本科   40000          高\n","101      03-16        本科   37500          高\n","16       03-16        本科   37500          高\n","131      03-16        硕士   37500          高\n","..         ...       ...     ...        ...\n","123      03-16        本科    4500          低\n","126      03-16        本科    4000          低\n","110      03-16        本科    4000          低\n","96       03-16        不限    3500          低\n","113      03-16        本科    3500          低\n","\n","[135 rows x 4 columns]"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["df.sort_values('salary', ascending=False)"]},{"cell_type":"markdown","metadata":{},"source":["### 30.取出第33行数据"]},{"cell_type":"code","execution_count":46,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["createTime    03-16\n","education        硕士\n","salary        22500\n","categories        高\n","Name: 32, dtype: object"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["df.loc[32]"]},{"cell_type":"markdown","metadata":{},"source":["### 31.计算salary列的中位数"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[{"data":{"text/plain":["17500.0"]},"execution_count":47,"metadata":{},"output_type":"execute_result"}],"source":["np.median(df['salary'])"]},{"cell_type":"markdown","metadata":{},"source":["### 32.绘制薪资水平频率分布直方图"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x11d2f5c50>"]},"execution_count":49,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["#执行两次\n","df.salary.plot(kind='hist')"]},{"cell_type":"markdown","metadata":{},"source":["### 33.绘制薪资水平密度曲线"]},{"cell_type":"code","execution_count":50,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x11d3828d0>"]},"execution_count":50,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["df.salary.plot(kind='kde',xlim=(0,80000))"]},{"cell_type":"markdown","metadata":{},"source":["### 34.删除最后一列categories"]},{"cell_type":"code","execution_count":51,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>14000</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 3 columns</p>\n","</div>"],"text/plain":["    createTime education  salary\n","0        03-16        本科   27500\n","1        03-16        本科   30000\n","2        03-16        不限   27500\n","3        03-16        本科   16500\n","4        03-16        本科   15000\n","..         ...       ...     ...\n","130      03-16        本科   14000\n","131      03-16        硕士   37500\n","132      03-16        本科   30000\n","133      03-16        本科   19000\n","134      03-16        本科   30000\n","\n","[135 rows x 3 columns]"]},"execution_count":51,"metadata":{},"output_type":"execute_result"}],"source":["del df['categories']\n","# 等价于\n","df.drop(columns=['categories'], inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["### 35.将df的第一列与第二列合并为新的一列"]},{"cell_type":"code","execution_count":52,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>test</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","      <td>本科03-16</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","      <td>不限03-16</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","      <td>本科03-16</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","      <td>本科03-16</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>14000</td>\n","      <td>本科03-16</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>硕士03-16</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","      <td>本科03-16</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 4 columns</p>\n","</div>"],"text/plain":["    createTime education  salary     test\n","0        03-16        本科   27500  本科03-16\n","1        03-16        本科   30000  本科03-16\n","2        03-16        不限   27500  不限03-16\n","3        03-16        本科   16500  本科03-16\n","4        03-16        本科   15000  本科03-16\n","..         ...       ...     ...      ...\n","130      03-16        本科   14000  本科03-16\n","131      03-16        硕士   37500  硕士03-16\n","132      03-16        本科   30000  本科03-16\n","133      03-16        本科   19000  本科03-16\n","134      03-16        本科   30000  本科03-16\n","\n","[135 rows x 4 columns]"]},"execution_count":52,"metadata":{},"output_type":"execute_result"}],"source":["df['test'] = df['education']+df['createTime']\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 36.将education列与salary列合并为新的一列"]},{"cell_type":"code","execution_count":53,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>test</th>\n","      <th>test1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","      <td>本科03-16</td>\n","      <td>27500本科</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","      <td>不限03-16</td>\n","      <td>27500不限</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","      <td>本科03-16</td>\n","      <td>16500本科</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","      <td>本科03-16</td>\n","      <td>15000本科</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>14000</td>\n","      <td>本科03-16</td>\n","      <td>14000本科</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>硕士03-16</td>\n","      <td>37500硕士</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","      <td>本科03-16</td>\n","      <td>19000本科</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 5 columns</p>\n","</div>"],"text/plain":["    createTime education  salary     test    test1\n","0        03-16        本科   27500  本科03-16  27500本科\n","1        03-16        本科   30000  本科03-16  30000本科\n","2        03-16        不限   27500  不限03-16  27500不限\n","3        03-16        本科   16500  本科03-16  16500本科\n","4        03-16        本科   15000  本科03-16  15000本科\n","..         ...       ...     ...      ...      ...\n","130      03-16        本科   14000  本科03-16  14000本科\n","131      03-16        硕士   37500  硕士03-16  37500硕士\n","132      03-16        本科   30000  本科03-16  30000本科\n","133      03-16        本科   19000  本科03-16  19000本科\n","134      03-16        本科   30000  本科03-16  30000本科\n","\n","[135 rows x 5 columns]"]},"execution_count":53,"metadata":{},"output_type":"execute_result"}],"source":["#备注：salary为int类型，操作与35题有所不同\n","df[\"test1\"] = df[\"salary\"].map(str) + df['education']\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 37.计算salary最大值与最小值之差"]},{"cell_type":"code","execution_count":227,"metadata":{},"outputs":[{"data":{"text/plain":["salary    41500\n","dtype: int64"]},"execution_count":227,"metadata":{},"output_type":"execute_result"}],"source":["df[['salary']].apply(lambda x: x.max() - x.min())"]},{"cell_type":"markdown","metadata":{},"source":["### 38.将第一行与最后一行拼接"]},{"cell_type":"code","execution_count":55,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>test</th>\n","      <th>test1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","      <td>本科03-16</td>\n","      <td>27500本科</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","      <td>本科03-16</td>\n","      <td>19000本科</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    createTime education  salary     test    test1\n","0        03-16        本科   27500  本科03-16  27500本科\n","133      03-16        本科   19000  本科03-16  19000本科"]},"execution_count":55,"metadata":{},"output_type":"execute_result"}],"source":["pd.concat([df[:1], df[-2:-1]])"]},{"cell_type":"markdown","metadata":{},"source":["### 39.将第8行数据添加至末尾"]},{"cell_type":"code","execution_count":56,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>test</th>\n","      <th>test1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","      <td>本科03-16</td>\n","      <td>27500本科</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","      <td>不限03-16</td>\n","      <td>27500不限</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","      <td>本科03-16</td>\n","      <td>16500本科</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","      <td>本科03-16</td>\n","      <td>15000本科</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>硕士03-16</td>\n","      <td>37500硕士</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","      <td>本科03-16</td>\n","      <td>19000本科</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>12500</td>\n","      <td>本科03-16</td>\n","      <td>12500本科</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>136 rows × 5 columns</p>\n","</div>"],"text/plain":["    createTime education  salary     test    test1\n","0        03-16        本科   27500  本科03-16  27500本科\n","1        03-16        本科   30000  本科03-16  30000本科\n","2        03-16        不限   27500  不限03-16  27500不限\n","3        03-16        本科   16500  本科03-16  16500本科\n","4        03-16        本科   15000  本科03-16  15000本科\n","..         ...       ...     ...      ...      ...\n","131      03-16        硕士   37500  硕士03-16  37500硕士\n","132      03-16        本科   30000  本科03-16  30000本科\n","133      03-16        本科   19000  本科03-16  19000本科\n","134      03-16        本科   30000  本科03-16  30000本科\n","7        03-16        本科   12500  本科03-16  12500本科\n","\n","[136 rows x 5 columns]"]},"execution_count":56,"metadata":{},"output_type":"execute_result"}],"source":["df.append(df.iloc[7])"]},{"cell_type":"markdown","metadata":{},"source":["### 40.查看每列的数据类型"]},{"cell_type":"code","execution_count":57,"metadata":{},"outputs":[{"data":{"text/plain":["createTime    object\n","education     object\n","salary         int64\n","test          object\n","test1         object\n","dtype: object"]},"execution_count":57,"metadata":{},"output_type":"execute_result"}],"source":["df.dtypes"]},{"cell_type":"markdown","metadata":{},"source":["### 41.将createTime列设置为索引"]},{"cell_type":"code","execution_count":58,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>test</th>\n","      <th>test1</th>\n","    </tr>\n","    <tr>\n","      <th>createTime</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>03-16</th>\n","      <td>本科</td>\n","      <td>27500</td>\n","      <td>本科03-16</td>\n","      <td>27500本科</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>不限</td>\n","      <td>27500</td>\n","      <td>不限03-16</td>\n","      <td>27500不限</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>本科</td>\n","      <td>16500</td>\n","      <td>本科03-16</td>\n","      <td>16500本科</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>本科</td>\n","      <td>15000</td>\n","      <td>本科03-16</td>\n","      <td>15000本科</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>本科</td>\n","      <td>14000</td>\n","      <td>本科03-16</td>\n","      <td>14000本科</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>硕士03-16</td>\n","      <td>37500硕士</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>本科</td>\n","      <td>19000</td>\n","      <td>本科03-16</td>\n","      <td>19000本科</td>\n","    </tr>\n","    <tr>\n","      <th>03-16</th>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 4 columns</p>\n","</div>"],"text/plain":["           education  salary     test    test1\n","createTime                                    \n","03-16             本科   27500  本科03-16  27500本科\n","03-16             本科   30000  本科03-16  30000本科\n","03-16             不限   27500  不限03-16  27500不限\n","03-16             本科   16500  本科03-16  16500本科\n","03-16             本科   15000  本科03-16  15000本科\n","...              ...     ...      ...      ...\n","03-16             本科   14000  本科03-16  14000本科\n","03-16             硕士   37500  硕士03-16  37500硕士\n","03-16             本科   30000  本科03-16  30000本科\n","03-16             本科   19000  本科03-16  19000本科\n","03-16             本科   30000  本科03-16  30000本科\n","\n","[135 rows x 4 columns]"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["df.set_index(\"createTime\")"]},{"cell_type":"markdown","metadata":{},"source":["### 42.生成一个和df长度相同的随机数dataframe"]},{"cell_type":"code","execution_count":59,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>9</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 1 columns</p>\n","</div>"],"text/plain":["     0\n","0    2\n","1    1\n","2    4\n","3    7\n","4    4\n","..  ..\n","130  4\n","131  7\n","132  4\n","133  6\n","134  9\n","\n","[135 rows x 1 columns]"]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["df1 = pd.DataFrame(pd.Series(np.random.randint(1, 10, 135)))\n","df1"]},{"cell_type":"markdown","metadata":{},"source":["### 43.将上一题生成的dataframe与df合并"]},{"cell_type":"code","execution_count":60,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>test</th>\n","      <th>test1</th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","      <td>本科03-16</td>\n","      <td>27500本科</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","      <td>不限03-16</td>\n","      <td>27500不限</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","      <td>本科03-16</td>\n","      <td>16500本科</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","      <td>本科03-16</td>\n","      <td>15000本科</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>14000</td>\n","      <td>本科03-16</td>\n","      <td>14000本科</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>硕士03-16</td>\n","      <td>37500硕士</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","      <td>本科03-16</td>\n","      <td>19000本科</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","      <td>9</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 6 columns</p>\n","</div>"],"text/plain":["    createTime education  salary     test    test1  0\n","0        03-16        本科   27500  本科03-16  27500本科  2\n","1        03-16        本科   30000  本科03-16  30000本科  1\n","2        03-16        不限   27500  不限03-16  27500不限  4\n","3        03-16        本科   16500  本科03-16  16500本科  7\n","4        03-16        本科   15000  本科03-16  15000本科  4\n","..         ...       ...     ...      ...      ... ..\n","130      03-16        本科   14000  本科03-16  14000本科  4\n","131      03-16        硕士   37500  硕士03-16  37500硕士  7\n","132      03-16        本科   30000  本科03-16  30000本科  4\n","133      03-16        本科   19000  本科03-16  19000本科  6\n","134      03-16        本科   30000  本科03-16  30000本科  9\n","\n","[135 rows x 6 columns]"]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["df= pd.concat([df,df1],axis=1)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 44.生成新的一列new为salary列减去之前生成随机数列"]},{"cell_type":"code","execution_count":62,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>test</th>\n","      <th>test1</th>\n","      <th>0</th>\n","      <th>new</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>27500</td>\n","      <td>本科03-16</td>\n","      <td>27500本科</td>\n","      <td>2</td>\n","      <td>27498</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","      <td>1</td>\n","      <td>29999</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>03-16</td>\n","      <td>不限</td>\n","      <td>27500</td>\n","      <td>不限03-16</td>\n","      <td>27500不限</td>\n","      <td>4</td>\n","      <td>27496</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>16500</td>\n","      <td>本科03-16</td>\n","      <td>16500本科</td>\n","      <td>7</td>\n","      <td>16493</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>15000</td>\n","      <td>本科03-16</td>\n","      <td>15000本科</td>\n","      <td>4</td>\n","      <td>14996</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>14000</td>\n","      <td>本科03-16</td>\n","      <td>14000本科</td>\n","      <td>4</td>\n","      <td>13996</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>硕士03-16</td>\n","      <td>37500硕士</td>\n","      <td>7</td>\n","      <td>37493</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","      <td>4</td>\n","      <td>29996</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>19000</td>\n","      <td>本科03-16</td>\n","      <td>19000本科</td>\n","      <td>6</td>\n","      <td>18994</td>\n","    </tr>\n","    <tr>\n","      <th>134</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>30000</td>\n","      <td>本科03-16</td>\n","      <td>30000本科</td>\n","      <td>9</td>\n","      <td>29991</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>135 rows × 7 columns</p>\n","</div>"],"text/plain":["    createTime education  salary     test    test1  0    new\n","0        03-16        本科   27500  本科03-16  27500本科  2  27498\n","1        03-16        本科   30000  本科03-16  30000本科  1  29999\n","2        03-16        不限   27500  不限03-16  27500不限  4  27496\n","3        03-16        本科   16500  本科03-16  16500本科  7  16493\n","4        03-16        本科   15000  本科03-16  15000本科  4  14996\n","..         ...       ...     ...      ...      ... ..    ...\n","130      03-16        本科   14000  本科03-16  14000本科  4  13996\n","131      03-16        硕士   37500  硕士03-16  37500硕士  7  37493\n","132      03-16        本科   30000  本科03-16  30000本科  4  29996\n","133      03-16        本科   19000  本科03-16  19000本科  6  18994\n","134      03-16        本科   30000  本科03-16  30000本科  9  29991\n","\n","[135 rows x 7 columns]"]},"execution_count":62,"metadata":{},"output_type":"execute_result"}],"source":["df[\"new\"] = df[\"salary\"] - df[0]\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 45.检查数据中是否含有任何缺失值"]},{"cell_type":"code","execution_count":63,"metadata":{},"outputs":[{"data":{"text/plain":["False"]},"execution_count":63,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().values.any()"]},{"cell_type":"markdown","metadata":{},"source":["### 46.将salary列类型转换为浮点数"]},{"cell_type":"code","execution_count":64,"metadata":{},"outputs":[{"data":{"text/plain":["0      27500.0\n","1      30000.0\n","2      27500.0\n","3      16500.0\n","4      15000.0\n","        ...   \n","130    14000.0\n","131    37500.0\n","132    30000.0\n","133    19000.0\n","134    30000.0\n","Name: salary, Length: 135, dtype: float64"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["df['salary'].astype(np.float64)"]},{"cell_type":"markdown","metadata":{},"source":["### 47.计算salary大于10000的次数"]},{"cell_type":"code","execution_count":65,"metadata":{},"outputs":[{"data":{"text/plain":["119"]},"execution_count":65,"metadata":{},"output_type":"execute_result"}],"source":["len(df[df['salary']>10000])"]},{"cell_type":"markdown","metadata":{},"source":["### 48.查看每种学历出现的次数"]},{"cell_type":"code","execution_count":66,"metadata":{},"outputs":[{"data":{"text/plain":["本科    119\n","硕士      7\n","不限      5\n","大专      4\n","Name: education, dtype: int64"]},"execution_count":66,"metadata":{},"output_type":"execute_result"}],"source":["df.education.value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["### 49.查看education列共有几种学历"]},{"cell_type":"code","execution_count":67,"metadata":{},"outputs":[{"data":{"text/plain":["4"]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["df['education'].nunique()"]},{"cell_type":"markdown","metadata":{},"source":["### 50.提取salary与new列的和大于60000的最后3行"]},{"cell_type":"code","execution_count":68,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>createTime</th>\n","      <th>education</th>\n","      <th>salary</th>\n","      <th>test</th>\n","      <th>test1</th>\n","      <th>0</th>\n","      <th>new</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>92</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>35000</td>\n","      <td>本科03-16</td>\n","      <td>35000本科</td>\n","      <td>8</td>\n","      <td>34992</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>03-16</td>\n","      <td>本科</td>\n","      <td>37500</td>\n","      <td>本科03-16</td>\n","      <td>37500本科</td>\n","      <td>2</td>\n","      <td>37498</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>03-16</td>\n","      <td>硕士</td>\n","      <td>37500</td>\n","      <td>硕士03-16</td>\n","      <td>37500硕士</td>\n","      <td>7</td>\n","      <td>37493</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    createTime education  salary     test    test1  0    new\n","92       03-16        本科   35000  本科03-16  35000本科  8  34992\n","101      03-16        本科   37500  本科03-16  37500本科  2  37498\n","131      03-16        硕士   37500  硕士03-16  37500硕士  7  37493"]},"execution_count":68,"metadata":{},"output_type":"execute_result"}],"source":["df1 = df[['salary','new']]\n","rowsums = df1.apply(np.sum, axis=1)\n","res = df.iloc[np.where(rowsums > 60000)[0][-3:], :]\n","res"]},{"cell_type":"markdown","metadata":{},"source":["## 第三期 金融数据处理"]},{"cell_type":"markdown","metadata":{},"source":["### 51.使用绝对路径读取本地Excel数据"]},{"cell_type":"code","execution_count":70,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"]}],"source":["#请将下面的路径替换为你存储数据的路径\n","data = pd.read_excel('/Users/Desktop/600000.SH.xls')"]},{"cell_type":"markdown","metadata":{},"source":["### 52.查看数据前三行"]},{"cell_type":"code","execution_count":71,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>代码</th>\n","      <th>简称</th>\n","      <th>日期</th>\n","      <th>前收盘价(元)</th>\n","      <th>开盘价(元)</th>\n","      <th>最高价(元)</th>\n","      <th>最低价(元)</th>\n","      <th>收盘价(元)</th>\n","      <th>成交量(股)</th>\n","      <th>成交金额(元)</th>\n","      <th>涨跌(元)</th>\n","      <th>涨跌幅(%)</th>\n","      <th>均价(元)</th>\n","      <th>换手率(%)</th>\n","      <th>A股流通市值(元)</th>\n","      <th>总市值(元)</th>\n","      <th>A股流通股本(股)</th>\n","      <th>市盈率</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-01-04</td>\n","      <td>16.1356</td>\n","      <td>16.1444</td>\n","      <td>16.1444</td>\n","      <td>15.4997</td>\n","      <td>15.7205</td>\n","      <td>42240610</td>\n","      <td>754425783</td>\n","      <td>-0.4151</td>\n","      <td>-2.5725</td>\n","      <td>17.8602</td>\n","      <td>0.2264</td>\n","      <td>3.320318e+11</td>\n","      <td>3.320318e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.5614</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-01-05</td>\n","      <td>15.7205</td>\n","      <td>15.4644</td>\n","      <td>15.9501</td>\n","      <td>15.3672</td>\n","      <td>15.8618</td>\n","      <td>58054793</td>\n","      <td>1034181474</td>\n","      <td>0.1413</td>\n","      <td>0.8989</td>\n","      <td>17.8139</td>\n","      <td>0.3112</td>\n","      <td>3.350163e+11</td>\n","      <td>3.350163e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.6204</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-01-06</td>\n","      <td>15.8618</td>\n","      <td>15.8088</td>\n","      <td>16.0208</td>\n","      <td>15.6234</td>\n","      <td>15.9855</td>\n","      <td>46772653</td>\n","      <td>838667398</td>\n","      <td>0.1236</td>\n","      <td>0.7795</td>\n","      <td>17.9307</td>\n","      <td>0.2507</td>\n","      <td>3.376278e+11</td>\n","      <td>3.376278e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.6720</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n","0  600000.SH  浦发银行 2016-01-04  16.1356  16.1444  16.1444  15.4997  15.7205   \n","1  600000.SH  浦发银行 2016-01-05  15.7205  15.4644  15.9501  15.3672  15.8618   \n","2  600000.SH  浦发银行 2016-01-06  15.8618  15.8088  16.0208  15.6234  15.9855   \n","\n","     成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n","0  42240610   754425783 -0.4151 -2.5725  17.8602  0.2264  3.320318e+11   \n","1  58054793  1034181474  0.1413  0.8989  17.8139  0.3112  3.350163e+11   \n","2  46772653   838667398  0.1236  0.7795  17.9307  0.2507  3.376278e+11   \n","\n","         总市值(元)     A股流通股本(股)     市盈率  \n","0  3.320318e+11  1.865347e+10  6.5614  \n","1  3.350163e+11  1.865347e+10  6.6204  \n","2  3.376278e+11  1.865347e+10  6.6720  "]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["data.head(3)"]},{"cell_type":"markdown","metadata":{},"source":["### 53.查看每列数据缺失值情况"]},{"cell_type":"code","execution_count":72,"metadata":{},"outputs":[{"data":{"text/plain":["代码           1\n","简称           2\n","日期           2\n","前收盘价(元)      2\n","开盘价(元)       2\n","最高价(元)       2\n","最低价(元)       2\n","收盘价(元)       2\n","成交量(股)       2\n","成交金额(元)      2\n","涨跌(元)        2\n","涨跌幅(%)       2\n","均价(元)        2\n","换手率(%)       2\n","A股流通市值(元)    2\n","总市值(元)       2\n","A股流通股本(股)    2\n","市盈率          2\n","dtype: int64"]},"execution_count":72,"metadata":{},"output_type":"execute_result"}],"source":["data.isnull().sum()"]},{"cell_type":"markdown","metadata":{},"source":["### 54.提取日期列含有空值的行"]},{"cell_type":"code","execution_count":73,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>代码</th>\n","      <th>简称</th>\n","      <th>日期</th>\n","      <th>前收盘价(元)</th>\n","      <th>开盘价(元)</th>\n","      <th>最高价(元)</th>\n","      <th>最低价(元)</th>\n","      <th>收盘价(元)</th>\n","      <th>成交量(股)</th>\n","      <th>成交金额(元)</th>\n","      <th>涨跌(元)</th>\n","      <th>涨跌幅(%)</th>\n","      <th>均价(元)</th>\n","      <th>换手率(%)</th>\n","      <th>A股流通市值(元)</th>\n","      <th>总市值(元)</th>\n","      <th>A股流通股本(股)</th>\n","      <th>市盈率</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>327</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaT</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>328</th>\n","      <td>数据来源：Wind资讯</td>\n","      <td>NaN</td>\n","      <td>NaT</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              代码   简称  日期  前收盘价(元)  开盘价(元)  最高价(元)  最低价(元)  收盘价(元) 成交量(股)  \\\n","327          NaN  NaN NaT      NaN     NaN     NaN     NaN     NaN    NaN   \n","328  数据来源：Wind资讯  NaN NaT      NaN     NaN     NaN     NaN     NaN    NaN   \n","\n","    成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)  A股流通市值(元)  总市值(元)  A股流通股本(股)  市盈率  \n","327     NaN    NaN     NaN   NaN    NaN        NaN     NaN        NaN  NaN  \n","328     NaN    NaN     NaN   NaN    NaN        NaN     NaN        NaN  NaN  "]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["data[data['日期'].isnull()]"]},{"cell_type":"markdown","metadata":{},"source":["### 55.输出每列缺失值具体行数"]},{"cell_type":"code","execution_count":74,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["列名：\"代码\", 第[327]行位置有缺失值\n","列名：\"简称\", 第[327, 328]行位置有缺失值\n","列名：\"日期\", 第[327, 328]行位置有缺失值\n","列名：\"前收盘价(元)\", 第[327, 328]行位置有缺失值\n","列名：\"开盘价(元)\", 第[327, 328]行位置有缺失值\n","列名：\"最高价(元)\", 第[327, 328]行位置有缺失值\n","列名：\"最低价(元)\", 第[327, 328]行位置有缺失值\n","列名：\"收盘价(元)\", 第[327, 328]行位置有缺失值\n","列名：\"成交量(股)\", 第[327, 328]行位置有缺失值\n","列名：\"成交金额(元)\", 第[327, 328]行位置有缺失值\n","列名：\"涨跌(元)\", 第[327, 328]行位置有缺失值\n","列名：\"涨跌幅(%)\", 第[327, 328]行位置有缺失值\n","列名：\"均价(元)\", 第[327, 328]行位置有缺失值\n","列名：\"换手率(%)\", 第[327, 328]行位置有缺失值\n","列名：\"A股流通市值(元)\", 第[327, 328]行位置有缺失值\n","列名：\"总市值(元)\", 第[327, 328]行位置有缺失值\n","列名：\"A股流通股本(股)\", 第[327, 328]行位置有缺失值\n","列名：\"市盈率\", 第[327, 328]行位置有缺失值\n"]}],"source":["for columname in data.columns:\n","    if data[columname].count() != len(data):\n","        loc = data[columname][data[columname].isnull().values==True].index.tolist()\n","        print('列名：\"{}\", 第{}行位置有缺失值'.format(columname,loc))"]},{"cell_type":"markdown","metadata":{},"source":["### 56.删除所有存在缺失值的行"]},{"cell_type":"code","execution_count":75,"metadata":{},"outputs":[],"source":["'''\n","备注\n","axis：0-行操作（默认），1-列操作\n","how：any-只要有空值就删除（默认），all-全部为空值才删除\n","inplace：False-返回新的数据集（默认），True-在原数据集上操作\n","'''\n","data.dropna(axis=0, how='any', inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["### 57.绘制收盘价的折线图"]},{"cell_type":"code","execution_count":77,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12b7a8b10>"]},"execution_count":77,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 600x450 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt \n","plt.style.use('seaborn-darkgrid') # 设置画图的风格\n","plt.rc('font',  size=6) #设置图中字体和大小\n","plt.rc('figure', figsize=(4,3), dpi=150) # 设置图的大小\n","data['收盘价(元)'].plot()\n","\n","# 等价于\n","import matplotlib.pyplot as plt\n","plt.plot(df['收盘价(元)'])"]},{"cell_type":"markdown","metadata":{},"source":["### 58.同时绘制开盘价与收盘价"]},{"cell_type":"code","execution_count":78,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12b997410>"]},"execution_count":78,"metadata":{},"output_type":"execute_result"},{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 25910 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 30424 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20215 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20803 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 24320 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 25910 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 30424 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20215 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20803 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 24320 missing from current font.\n","  font.set_text(s, 0, 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zOJEQW4ODlimK2Pq1Es/rHgoegt+GrkdddwIREZUITTx27AIHP4mfgNSp7tC8NaDd8dqPRDEQCDhQBAEkSY0ekLIUqVbMrOcVtk+wrh/gZ/Jfbf/LM7Dz8e8jPZl74LZ8mAReLQiF37E+nJzkcQtnQmiL5BwIAiCSBOavCE4obY4yMIhu3Kh4fygShhMKc0GONnCYBbkr/YQzMp+Tuw+qJIgegsJB4IgiDSh0RNCFqcXDiMnn4UWlqObr7YoTC6JFX2ykHAgUggJB4IgiDShKYGrgucF+LMn6OarsyoqVcLBLMiWh66gSgBAlIQDkRxIOBAEQaQBosTgDkbh5FTCwRKrAFkyYpTumIDK4pBnj4kEq6nL4hATFmRxIJIFCQeCIIg0wBuKggGaGAfJnB177SjWHaMWDmoMYxzI4kAkCRIOBEEQaYAnFAXAtK4KlcWBGQiHrqyKX3xrimbcKMYBZHEgkoRpqBdAEARBAO5gFA6EwHOxvhJdMQ4AIDmKdMfct3gGXDlTMHt0rmbcKMaBE/VdNQmiP5BwIAiCSAM8wajGTQFoLQ6SXW9xmDSyGGJ+nm7cYiJXBZE6kuaqWL16NR544AEAwLPPPouqqirlvzPPPBOVlZXYvl3faCUYDOLee+/FnDlzMG/ePLz99tvJWhJBEETG0BGMIEsdGMmbAMGmbBtZHJjZbngunuNg4jlyVRApYcAWB1EU8fzzz+OJJ57AFVdcAQBYsWIFVqxYocx55JFH0NDQgNmzZ+uOf/zxxxEIBLB+/XocPnwYt956K6ZOnYopU6bo5hIEQZyueEJRZKmLP5mdSkEnwNjiwEzGwgGQ4xyClFVBpIABC4f7778fHo8Hy5cvRzgc1u3fsWMH1qxZgw8++MDw+DVr1uD3v/897HY7ZsyYgSVLluDdd98l4UAQxLDCHYxqiz+p4huARBYHR8LzmQUOoSi5KojkM2DhcN9996GkpAQrV65EbW2tbv+jjz6KO++8E7m5ubp9HR0daG1tRUVFhTI2fvx4bNiwoVfXFgQe+fmJ/+EMV4TOiGq6N3ro3nQP3Z/EpPreRMDBqcqo4O3ZcdfSXze/ME9jlVBjMwsIRS2qbRGWFK2dPjfdc7rdnwHHOJSUlCTct337dtTU1GDZsmWG+wMBWV3b7TFzm81mQzBI0b8EQQwv2gMRjasC1uzEk7tIIBoAOUBSE+NAFgciSaQ0q+Kdd97B0qVLYbFYDPfbbHLgTzAYhMPh0L3uCVGU4HJRx7d4ulQt3Rs9dG+6h+5PYlJ9b1rcQYxSuSoinAMdcdcq4gRwTFS2u1uLwGmzKkI+H7wpWjt9bronXe9PcXEvxKkBKS0AtW7dOixatCjh/ry8PBQUFOD48ePK2LFjxzB+/PhULosgCCLt8AQjGleFOhVTGTPrxxJhEXiq40CkhJQJh5aWFjQ3N2P69Ondzrv88svx5JNPwuv1orq6GmvWrMGSJUtStSyCIIi0RO5TkTg4EjAWE4mId1VQVgWRLFImHOrq6pCbm2vopqiqqsJXX30FAPjJT36C/Px8LFy4EHfeeScefPBBTJ06NVXLIgiCSEvcwSiy1X0qLAbCoQ8WB7PAa5pcUYwDkSySFuNw1113abZnzpyJL774wnDujh07lNcOhwOPPPJIspZBEMQAaPOFce+fd2F+659wg2ktIlU/QPCM7w31soYFnlBU66owsDiER8+FyXW4V+ezCBxZHIiUQE2uCIJQ+N9/HMKuQ8fxT/7/g819BNmfPwBEAz0fSPSb6no3fvDqVvyH9Ay+Y/pcGTdyS/jPvQ/R3PFgJgc6vvWHbs9r1sU4kHAgkgP1qiAIQuGPW06hgnNrxvigC1JW4gqFxMB49ovjKGj6AtdaPtOMG8c4ZMN1wzogGgQSlJvuwkrpmESKIIsDQRAKZoEDg7Y2ABdJrxSy043NJ9pxHr9XN84MYhwAyLUbehANQFeMw/CwOAQjIh7/9Aj+66OD8ASjQ72c0x6yOBAEoWA1CbCEI5oxLuROMJtIFi5mFAiZQDj0EovAoWOY9Kr4274mvLZdrlzcEYzg11d0n81HDAyyOBAEoWAz87AiTjiESTikErPAabpidpHQ4tDr82pjHE5nV8XG4y7l9WeHW9HgppoVqYQsDgRBKFhNeuHAk8UhZYgSQ0RkyDLpA1CZyWZwRO+x6FwVGf4wjQZg2/saYLIjWLkMECywHPsEQtsBjLScAwCYxNVgmbAO/jVvwjp7HkKV13RblpvoHyQcCIJQsJkFWDmyOAwW/rBcPlpd+AkAJGsuooXTBnRuXYxDNLOFg33XamR9+T8AANueV+A/607kfvgDAMDl2d/E67gBfzD/GmP4ZsAF4B9/RYclC+GKy4Zw1acn5KogCELBYuJhRVgzRjEOqcMXlgP51IWfIvmT4bpmTa8CILvDajKo48DYgM45lJjrt8ReN+1URAMAnO/5GwrhlkWD+pjaLwdtfcMJEg4EQSiQq2Jw8XZaHNRdMYOzfgApb+D9euQYh7jKvVLYeHIGwHvqut2fy/l0Y0LHiVQtZ1hDwoEgCAWbSTAIjvQM0WpOf3wh2eKQxalLTfevY2E88TEOAMBlcICk2FHb7f7JXI1uTHCfTNVyhjUkHAiC0GDhtHnwXKhjiFZy+uOP6C0OA82m6CI+xgEAkKkpmZEAbNHuP4fT+OO6McF9EmBSihY1fCHhQBCEQkSU9DEOFByZMnwhWThkq7tiJs3iwCGcSRYHKQq+47hhHAbv7d5NAQBTOb11gRND4H2NyVgdoYKEA0EQChGR6WMcyFWRMrqCI7XNrXrfAbM7LCYeEniEmaCMpW0RKElE3p+WoPCVC5G1/t91u93NsVgFH7ManmIabxzPILgpziHZkHAgCEJBtjhQ5cjBwhcWIUCEg4s90JNncZC/3jWttdNUOJjrNsHcUg0AsO9+UbfOlobYw/8AG4OvpMm6c5RyLt0YAPAdFOeQbEg4EAShEBYlfR0HinFIGb6wCCe0NRySGeMAICNqOfCBNs22qe2gZtvfEnv417FC/GfkRmyXJiIQnzViAFkckg8JB4IgFIxcFZRVkTp8IVFTwwEYeI+KLiwmuWJiJjS64gItmm1Tc7VmW1JlVNSzQuxiE3B1+D/x3bDerREPpWQmHxIOBEEoREQJlvgYh4gPkKjjYCrwhaOaVExmcgC80M0RvUdxVaj7VaSpcBDiAhhNLVrhYA3UK6/rWaHy+gQrMTyfm8WKZ5HFIfmQcCAIQsEoxgEgq0Oq8IVFTSqmlCQ3BaB2Vag6ZKZpVgXva9Bsqy0OLn8Y+WLMIlHPCmL7kA0P01fY3MfGKa+plkPyIeFAEIRCRGS6GAeAAiRThWxxUGVUJFE4WI1iHNLU4hCfMmlq2QtIcqrqrjoPRnKtyj61xQHg0MTydOfbJ42NnTvQCkT0TcSI/kPCgSAIBaM6DgDAUy2HlOCPszgkK6MCAMwGMQ7p6qqItzhw0QCEjmMAgIM19cjl/Mq+Oo1wANqgv2dHWRkiqjRUwaOvKkn0HxIOBEEoJHRVkMUhJfjCojbGIUmBkYBxjEM6uio8wSjCHfoCT6aWPQCAjpo9ylgANjQjVzOvlWm3AcDNnGhQuTR4Eg5JhYQDQRAAAMaYYVYFQCmZqcIXiiILsV/TyXRVGMY4iOmXjvnCF/vhkIwaVB1HVJRgajugjIXyJmJUngMAsHT6CJh4Dm1Mb3Hwwo5aFMXO5em+zwXRN0xDvQCCINKDqCSX+o3vVQFQcGSq8IXFuHLTybQ4ZIarYt3OPYBBMUi+4yQONvswnp1SxswlU/Ha/LNwtNWPKSOysOVkO1oCObpjfbChhhUD2AcAEDyndHOI/kMWB4IgAMiBkQCMYxzI4pB0GGPw6mIckigcTEYFoNJLONR2BBJWfBTcx7Gzzq3pesmKKmEzC5hWmg2e41DotKCN6YWDl9lRy2IWh+a6I8lf/DCGhANBEADk+AYAxq6KYPtgL+e0JywyiBKL61ORvOBIwxiHNLM4bD7RjhIkEg4nsfWEC5P4mJtBLNCWmi5yWtBqIBzyc/NRoxEOR3Gg0ZukVRMkHAiCAABEpC6Lg1448ME23RgxMLoaXKldFcms42DiDVwV0fRJS9x2qh3PfbIV95nfUMb2szHKa97bgBM1J1DGxT570YIpmnMUOS1ogT44cmxpcaerQmYOfxChna+npasmEyHhQBAEACDaZXEwqONAwiH5dLXUTpWrguM4lGRZ0MTylTFT855ujhg8dtR0YMWbO/Gi5VGM5mLFnbaLE8E4OY2SA8M54jZln2TOhpRVpjlPodNsGBw5cdQIjasCABYe+jmcG/87mW9j2ELCgSAIAOoYBwNXRaBVN0YMDG84iglcLaaq2kEnUzgAwIJJRfhSmqZsmxu3gQsPvcl+47E2lKBd1wp7m1SJgGOksn0pHxMOYsEkgOM082VXhd7iUFU+Ek0o1I1bj30y0KUTIOFAEEQnSlaFkasiYOyHJvrPnpMNeNnyCEZwsfgRKXt0Uq9xyeRi7GIV6GByCiMnRWGu25TUa/QZxmBtrcYkXltb4ReR7+Gv0gXYH4zVX1gkxIRDtHCq7lSFTitc0IutkXl2/GLpTN244DkFMDaQ1RMg4UAQRCfdBUeSqyL5tOxdi5Eq/71/9h2IlJ2T1GvMHJWDoiwbNkrTlTHzqXVJvUZfyVr/EB6s/SFetTyijB03T8Tz4rcgQsC+UJHhcZHRF+jGipxmiDBuCjZ/UhEaiufqxqkmycAh4UAQBAA5OJKDBKtRHYegC2DSEKzq9KTBHYTFdUjZbiudC995/6ozxQ8UnuMwb2IRNkgzlDHLqfVJvUafYAz23S/ohn22UuX1SYOOlwwcwqMv1I0XOi26MTX1Zz2A98WzNWNUfnrgkHAgCAKAHBxpgXH7bI6J9Estiaw91KIx1VvLzkjZtUbnaS0OJtchIDo0FST5BBUcw86Yi+YQG6XbHy0+A8xeoBvvSTjkjJqGH0V+jMNSLG6Cyk8PHBIOBEEA6OyMaVD8qQs+mKI4h84uiMOJvx9o1hQ2EgsqU3atIqcFp1SpiQDA+5tSdr3uUJeP1pAbEw4bpBloto7T7I6M0bscgFhZ7URkW02wmXhNaqZicSALWr8h4UAQBAAgIukbXLk7g+qA5GdWcGEP8t74JgpfOAvm2o1JPXc6U+8OYk99ByZyiQsbJZNCpwVRmNCsKpTEexu6OSJ1CAmEg6NovPI6DDO8l/4vGBd7PIXHzEt4zl8u1QdNdsFxHEZkWzWpmbynBs51D6Ho95Pg/PKXfVk+0QkJB4IgAHRaHOLiGxpVNQD4QHIDJB1bHoO5pRp8oAU5H/4gqedOZ/5+oBljuCbYOutlMHCI5k9K2fW6zPmNqm6Rgq8xZdfrDlPbQcPxklETcMWMUuTZzbh/4URkjfsGPAt+A9E5AoGp1yEy6vyE51w4uRihisuU7Wic9SZeOFiPfgTH7tXgxBAc258CIulTFCtToCZXBEEAkGMc1K6KKGdGK3IwCfIv42RmVmw81oazd72LLnvG6dYLgzGG9/Y04uWtpzA6z46SLCu+ONYGbyiKYFTCxSo3hZQzFjDbU7aWIkU45OMMHAcA8L7BtziIEkPDsZ0oN9qXPRr/vigP/74oNhaa+h2Epn6nV+f2XvRfEFyHwEX88FzyW82+EdnWOFeFtuEVH3RBSuH9Px0h4UAQBADoWmpLvAVt0VhVvmS6Kp7ZcBznSyEguUkEaYHEGP7n40N4p7oBPCSscP8Wc4Xd+DHj8Zk0CwHegttN7yvzoyl0UwCA0yLAZuK11qMhEA7rDjdjSeik4d+cWfVFnPqC5BwB13c/leMWeG16Zkm2FTuYcYonAHChdiB7ZML9hB4SDgRBANDHOEiCVVPON1nBkYwx7G/ywmrV14s4HXhrZz3eqZYfzJfxW3Cd6TN5Bwd8n9dXLkxlfAMg+/kLnRY0eGOuiqEQDgcP7YODS9ArIhlpqBwHcPqaDqVxrop4+FAHhl947sCgGAeCIAB0xTjEHuZMsKINauGQHFeFt7NHQ3cZHAPh0OZ3cPL5a3Dg85dTcv7uqGkP4MnPjyrb5/D7up3PBCuCU3pnjh8IRU4LGjG0Fodoi3FgJEux2Wn+pCKEbcUIMePfyVyIOr/2FbI4EAQBQO+qYNIQ0AUAACAASURBVIIVLpZ8V0WjR/7VqSs0xSSAG9hvmaP1TZi59afI4gKI7t6Cmsrz4CydOKBz9oXXt9ciFI3iu8KnOM98CEsRK7bEOB5cZwpgcOJSBKd9F9GSWWBWfVvoZFOUZUFjg1o4pDY4cmdtB96rbsSllcU4pzwfoaiESHud8sRpYTnIhQ9mToRn0VMpXUue3Yx3bzsX+/+8CGe2faDbzwdPr/iawYCEA0EQAICoJGn6VHAmK1pVKXxckrIqGr0hAPp+AVzE322TJ6F5D2z730S44puIjDpPtz8qSvjHh6/hnM421SZOQnTrs8DS3yRl3b3hVEsbVpt/jfnCTt0+17Ufg5mzwIkhiPkTBm1NAFDosGC3KsZB8DXIPRuSXKkSkGM87nt3L9r8EbxT3YDP7jofh5t9yGexB/SX0jT8r3ALXlw+EdaS1LpqAMBmFlB27TO464XX4etoxoOm1zCJl4N+qbBZ3yFXBUEQAPQWB95sg0vlqoA/eRYHJ/SVC7mwJ/FBTELuh7fCset55HzwT4YdHj852IzZ3s80Y2NP/VUulz1ILHS9YSgaJEs2xILJkHJGD7poAGSLQ4NKOHDRYMoemP6wiDZ/7HN0qMmHXXVuFHGx650xYQKeufmSQRENXXC8gDHT52KtNBs7pJgVioRD3yHhQBAEALnJlTXe4oA8ZdsUaEpKZ8FGT0jzEFGu141w+HrnFiWNjg+7IbTs1c3ZfbwOC/ivNWMWFoRtz6sDXHHvkBjDjLBeNABAdETVgN0wA6HQYYEL2Ro/f6rcFYGINtQwGBV1wqFkxGjkO7ovF50KirPka3bAqYydbqnAg0HSPsmrV6/GAw88oGxXV1dj2bJlqKqqwlVXXYVdu3YZHnfw4EFMnToVVVVVyn8fffRRspZFEEQviUja4EiYbPBaYg2HeDEELgkBkk2eEIrQe+Gw7VQ71n72oWZM8MaqLgrNe2A98BZKaj7Urr8T+64/AGJqAjHVtHhCmMydMtxn1BJ6MCnMsgDg0DQIKZn+sFY4eEMidtd7UKwSDpIjcZZDKinoFCsdLCYcKDiy7wxYOIiiiFWrVuFXv/qVMub1erFixQrceOON2L59O6677jr8+Mc/Njx+//79mD9/Pnbs2KH8t3jx4oEuiyCIPhJfAIoJVsCWBz+zKmOCt27A10lkcRADbsP5r3xVg9ncIc2Y4D4p/7/9KPL/ciVy/v7P+Jfw75T9H4tnIcTM8hx/E6yH3hnwunuitbkGeZxP2Y6UnqW8Dk1cmvLrd4dSBGoQMiviLQ57Gjxo9YU1YlFyFMcfNigUdgqHdsRiaXgSDn1mwMGR999/PzweD5YvX45wWP7SWbt2LSoqKnDllVcCAJYvX47p06dDkiTwvFar7N+/H1OmTBnoMgiCGCARkcERl1WRbTOj3l+ACVw9AID31gPFMxKdolc0ekKYzOlFgt/bDqP6fa2+MGbzccKh4wQAwLbvTXBRfcngd/NvQlvbW0oNBfvO5xGasnxA6+6JUEMs9bKNy4N0+Quw734R0YLJiI6YldJr90S+XRZRLSxWaClVTcv8ccJh3RE5NkYtFiX7EFkcnPJ9cKstDpRV0WcGLBzuu+8+lJSUYOXKlaitlc2H+/btw+jRo3H33Xdj8+bNmDBhAh5++GGdaACAAwcOIBwOY8GCBeA4Dtdeey1uv/32Xl1bEHjk5zt6njjMEDo7xtG90UP3JjG8WdC01bbYHSjMtqKutRATIAuHLKkFrB/3LhgMoG7XWpTn8Ih6vcauCslv+Hfhgy5M4Os1Y1Z/DUx5dkiH39PNr7adhaKJs/HClz5FOJhbqpHvEAFrtm5+b+nps2N1x8RNo3U8JpWNAsr+FVZA5VEfGiydv7R9sCljDiEEW5L+HajvjdDk0+w76QrAgghyOb8yll02Fsgd/H+DWTny+1fHOJiinpR/H5xu3zsDdlWUlJToxtxuN9577z1ceeWVWL9+PRYsWIAf/ehHiET0/se8vDwsWLAA77//PlatWoU333wTf/nLXwa6LIIg+khUlGDjVLEAJity7WY0qJojce5agyP1+EJR1Ln8QNsRfHm4EScfuwiTP74Rlj/fgM/4O/ANfr/umKBX/wvYF4pirGe7bjzccgR7t6+HxX1Ct+/IpJsxuSQbB9hojZsFCQoQaWg/BUT8Pc8zwOaKCQd39uBnTnSHwyKA5wAviwkHGGSmJANfOKobK0Schck5NBYHs8Aj32HWxDggMHhZN6cLKanjYDabMXv2bFx88cUAgFtvvRXPPvssjh49ispKbeeyxx57THk9ceJE3HDDDVi7di2uvvrqHq8jihJcrv79Iz+d6VK1dG/00L1JjDcQgU0V4xCUzLByQB0KlbFw8wl4erh3zd4QvvPCV/hX8Vlcb/oUZzI7criYO8HCibhQ2KM7ztfepv27iCGc2r8FT5h/p5tr9Tdi23vPYGbnN5iH2fG5dCY2SVNxyaSLkc0ABh6H2CicycmVHAPHdyHomGa4ZokxSF+/grKND0Ky5qHt+s/A4gL4uvvsBCMistyHY+8le0LafcacFhO8YswZFHK74E3SGtX3pqVd7zrSuCksOXB5JABDc3/y7Ga0B1TCIdgBV5svJTUtukjX753i4v5Z4FKSH1ReXg6PJxYhzRiDJElgcalcwWAQjz76qGZuOByG1WoFQRCDS1SUNMIBJhtybCbUs5hw4H31BkdqWb35FAKhMK43fQoAGtHQHVIw9qs03NGAnFfmY/Zn31XaT8fzXWGt8vp33LV4eeR/IPvcH2B6WQ7GF8pf1IfYaGUO15qg5DFjWPHGTpRtfBCAHCzn/PKRXq0ZAI63+nH5s19gdFRl/ShMv7gtp0WAj8WEAxfxdTO7/wRUWRVWhMFB0gqHIcqo6KIwzuLAMRFcJDXWl9OVlAiHxYsX49ixY/joo4+UrIuSkhKdtcFms2HDhg14+umnEYlEcPDgQbz66qv49re/nYplEQTRDRGRaVwVTLAhx2bWCAfB27Nw2FHToTdN9wIWckNiDF8cacXel34Iq1eb2viUeBVquVJlWy0ovrfsOjx1zUzccu5YAECW1YTSbCsOSqOUOXuqt6IjoBch+xq92FGrXa+lZkOv1/3m13W4QXwXOZ0+fJFxsI80tmwMJU6rAK8qxsGoiFYy6AqOnMkdwSbrndhmXaHp2SHZhyajoosChwXuuKgTCpDsGykRDmVlZXjuueewatUqzJkzB59++imefvppcJ2moKqqKnz11VcAgJUrV+LAgQM455xz8IMf/AArVqzAvHnzUrEsgiC6IRJncWAmG7JtJtSpLQ7eermnRDdwHFDK9b3eQ0NLC5au2oyvPnoOl/JbNfveEc9H5Ox7UDy6UnecZM6CpURfJ6GiyIGDKovDGPEkPj+ir37pMhATvL+51+uuO1aNH5v+rGz/VZqL0uIRvT5+sMiymDQWBz5VFodO4fD/TB8hn/OigPNihWmNsj/eBTTYFDgtECHAo7a+UBGoPpG0GIe77rpLs33WWWfhrbfeMpy7Y8cO5XV5eTlWr16drGUQBNFPIhLTCYccq0kbHClFwAVawRLk4UuMoaY9gAu5vgecZSMAeBvxL9bn0dUwcYM4HTdGHgQDj9+OyIMYKgdOfa45LjqiCuD17ZQrCp1Ye2yMsj2Sa0NdUxOAUs08TzAKDloxxEnhXvVyaHAHcYHvY1hNckBgA8vHydkP4iJr+rUBki0OqodldyW+B0BXAagr+S8M9w+1q6LAIadkdsApf+Ygu6eotXbvoZLTBEEAMIpxsCPfYYYHDs2vM1P7UYOjZRrcIQQiEkb0UjiInFl5ncP58Yj5OSVtz8PsWF/5EOxmM84tz8c54/IRGr9Idw51oSU1FYUO1KFQs/bS0HHdvPZABDkGgXq8v6nH9W8+4cJZ/EFlO+v8O/C9CwdW5yJVOC0mrasixRaHr5jeOgQMvaui0KB65OGaWlz7wlf42Yf7EZUGXlb9dIeEA0EQAAxiHEw2TC/Nxsgcq8Zdkf3BP8Fy7BPDcxxtlR9GiVwVrUwbxd1ccoHyehZ/BAuFmDXyBeetuHXxhVh75/lYuWwGBJ5DZMxFiBZoH0iJhMOEIicADodYLM5hVrM+1dsViCCf0//6FtoO6sbi+ep4C2ZyMSEVKftGj8cMFVnWuODIVMU4hGXrjbomiBrJqU/hH0y6ikCphcO7W/fjaKsfH+xtwkf7ehaMwx0SDgRBAJBdFfY4V4VJ4PHQ4kr8RZyrjAuhduR+cDMKXpiDnL/dBi4Qixs42iL/ch8BY4vDZkkbi+AdZRzPtEWqROP4a+Tr8Sp3AcchMONmzdxo6WzDc0wZkYWqUTl4VzxfGfuG5++wHPtYM68jEEEB9MLB1NZ93YfjrX40Hd0Ge6fYEjkzosVndHvMUDLYFodsAyuOZMtHeMz8lFy3t+QblJ3OF2Of4U8O9D6+ZbhCwoEgCABd3TG1WRUAMGdsHnxVP8S94RXavhW+BliPfADH1v9Vxo50WhwSuSo2xQkHfqSxteDZ6FLMGJVruC84dTkixTPl15OvBrMaz+M5Ds9eeyac5/4A21VtlO07fq+Z1x6III/T//ruzuIQlRh+/rcDmMFiRZ+ixWcAJlvCY4Yap0WAV21xEEMpaf7VlVWRzWmFQ2TEbLiWfwApe2TSr9kXCjtjHPZLY5Wxc/l9MCGKe0x/xndqfgFf68mhWl5GQMKBIAgAQDTOVaF+CN590XiUz7sZV0X+G/ulMZrjzCdi9RSOtXZaHBIIh7fEi9DI5Fbd/jNvgzOnUDfniFSGT6VZmFmWoDiNYEX7srfRdv1n8Fzy227fE89xKC/MxmPRWJ8KoeOYZo7LH0E+9MLB1I1w+OJoG/Y0eDTxDWIauykAOUXVF9cNJBVWh646Dl2BhwBwL36C9mXvQMoZk+iwQaM4ywqnRcAGKWYdOos/gPtMb+CfTX/BFcIXsH1wxxCuMP1Jv9BfgiCGhIgUn46p+nXKcbhu9ijMHHk5bntvHGZ5P8eTlqcAAGb3CfDuk5ByxqLVJx+fKMbhhZvmosO0DpZwHaJF0yEYpME9I34bZ48rQFFWN4XgBCvE/ImJ96vItZtxiqnag/ubATECCPIvT9niYBDj0KEvZ93Fl8fl9zeLi1WLjJQZW0/SBadF0PSqAOQ4B2bLT3BE/whERJgQhYMLKWNLL7kspZUZ+4LAc5hemo1tJyfAzRzI4fywcCJuM72vzCl1fw1Xw/aEbrDhDlkcCIIA0Bkcqe6OaWB2n1aajRe//w24K5bisBQzOVtOrQNjDB3BKGwIaRoaqSkvdCA/t0A263McmCVLsz+UX4n5V96B31wxPUnvCsixmdDIYg9HDgx8IObHbg9EkG/gquADzUDY+Bf55hMu2BDCWD52nugAu4amGqfVBAm8xt2UioqJgYiILGirhZ5RPirB7KFh5sgciBDwpZS4UJf961WDuKLMgoQDQRAAgEhU1PxKNBIOgGzyvvuiCqyXYg/K7M8eAHfoA4SiiVMx3UZuBd6EyIgqAIBkzYV3yYs4p7wQNrO+LkN/ybWZEIIF7aooet7bIF+TsYTBkQAgePS+7tqOAGragyjnGpUxJlghZaXXwzGeLIt8T7W1HJIvHPwRURPfwKAXiEPNzFE5AKD5DMdjPfoBOH/LYC0poyDhQBAEAIBncYFy3QT6jc6zYa+tSjNW/MntmM9/rcmokOzFaF/6CjoufxGhyVcZnqtjyUvwLPgVXMvfh5Qz2nDOQMi2yS4JtdXBevBtWA6vgTcQgMhgGBwJGLsrNp9oBwBUcHXKmJhbbliEKp1wdhalUnfITLZwYIwhEBaRo7I4MEsWwKXXo+aMUlk4/F2crVhgopZc3Ba+By4mixyOSRC6qVkynEmvvyZBEEMGFw1qtruyKgznchz4cRfq6jIsFTaiVGVxELPKEBk7H+HyhQkfHsyWj+C06yHllvd/8d1gNfGwmXiNcHDsXo3cj1bAuuM5ADAMjgSMhcPWE/L7G881KGNi3vhkLjklODstDr4UpmSGRQaRaTMqmKV/HRhTSbZNFlENKMR3w/+Gx/mbcOyKD/Gx9A1Nb5Zgez3WHmhA+PBamJqrh2q5aQcJB4IgEJUYuGhIM8bM9gSzZWaNH4Xvhx9Avaok9YV8NUZyMfOulFWW3IX2k/g4hy5Gf/0rANAER9ap3o/g1rsqTrjkX9MVfKzhl5hXkbS1poqsTouDOrOC74PF4aQrgD313Tcvi2VUpLdwAIAb58jWrZ1sIkrm342sQnm7qTPrBwDe3LADez9ciVEffR/5b34Tpsavh2St6QYJB4IgEIyI2qqR4ADe0u0x3xibh6OmCfhW6H8gMTlifgTXjgVC7MtVzE4Pv3+u3YwGFCTYyzTBkTulCcprwX1cN7src6SCiwmHaN4E3bx0oyvGQVM+vG5Tr3pW7K5z4/qXtuGmP36NP26rSTjPb1D8iVlz+rvklHLLuWNx67ljce+CCbh0SjFMAo9sqwnNLFYXxBRowsPmF5XtrPUPDcVS0w4SDgRBwB8WYUdcYGQP6XO5djMe/fY0uJCDPWycMn4Ov195LWUnP2ahP+QmsDgAQDHaka8KjlQLB75Da3EQJYb2QAQA08Y4ZIDFwWriIXBaV4X9wJ9R8MqFPcY6PLnuKEJRuZT0E58dVRpZxRMr/hSLcZDS1OKQZTXh9gvKcd3sUeA7P+t5dhOaELM4FEObLiy0HQJBwoEgCMgpdNoGV72rgHheeQEevGQiNiSIThfTRDjk2MwJhcMs/gisXKyvwk6msjh4agBJfhh6glGccvkhMaAAHk3KaSYIB47j5CJQTOuC4gOtsB5+L+Fxh5t9+Lq2y0XBMJZrxMbN6wxTVTPJVWFEnt2MJtXnJD5DiGOdgikaHNYZF1QAKgl8ebwNW/cdxPmud5EXTW2DlEhuBSZddi9Mlm6K4xBEH5GFQ/c1HBJRmmPDm9IZ+CH0D590sTjk2Ew4mkA4XCOs02xXS7FAR06KINpRi686snDP23uUzonjVW4KyZoLZkvkBkkvnBYB3qj+b8uFEscuvLZddk2MQjOesPwOZ/MHgF0A22tH+9Vva/pzGJWbZpb0dFUYkWs3a1wV0/i44FhJRLjtBMr+egW4YBs8C59AqHLZIK9y6CHhMEBqOwL4ydt78KjwNC4TNqT+gh7g80+smHb5vam/FjFs8MfHOJi6D4xUMzLHhl2SsY8/nSwODcz44b5Y+Ep5HbUVwBN0oInloYST0y7vf/lDrItoe2yoaziIuePTpipiT3hCoqZfRRdcNGAwW2bjMRfy4cY71odQxLk1x9h2r4b34seUsaBhjEPmWBzy7WbUq4Ijizmtq4KTwtjxyk8xSpCtDfadzw1L4UCuigGy5UQ7ohLDDO5Yz5OThKmbUrgE0R8CYW25aXSTihlPaY4VHjhwQtK2S2YmR9LLGfeXbKuAFhg3w+qCcTx837gXPAfUsCJlvETSWxFHc7GKkWLOWN3+dMUTiur6VQDQVNJU4wtH0eIL4zvC5xrR0IW5Ybtmu8vikKOJccgci0Oe3YwmdP+ZXSJ8qbw2tewFIsZVUk9nyOIwQKo705OsKjPvrvzFaLeUJvU6xS2bMVWUg84UPxtBJAl/RIQtPjiyl9jMAgocZlRHyjEOsYesmD06bX6Je0IipG5+J0mCBe4lLyMy+gIUbNyE2nARZkPuQ6EWCV2MUqecpknmSG+YW1EA7wn935ZP4K8/2Zl6WsmfUsa+EKfjAmEPAMDkOgQu2A7AAeD0iHFQuyp6gmMizE07ERl1XgpXlX6QcBgg1fVyNLaViwmHMQtWoCzJnfL2vvkgpjZ3CYdoD7MJom8EwiJsXP9iHAA5zmFPczkuF7YoY+mSigkAFYXyg+1fIrfhP0wvYw8rx7n8PmW//+x7ERl9AQCgOMuCmtZiZd8otGA8V4+nzE8iACtuC/9EIxzSxR3TG26YMxpf1enFXKJAv5NtsnCYzMVSMP8izsUU/iQKO2tfmBu3A2Vy3xJf2CjGIbOEQwA2eJhdkxnSHaaGbcNOOJCrYgB4Q1GljbDWzJv8wEWJi5Wz5cniQCQZf1xWRV9iHABgRLYVe5i2emK6BEYCwKWVxZhSkoV3uYvxzFkf427LL/CqtAgAEKq4DIFZK5S5xVlW1KpcFaO4FjxsegHT+ROYwx/EP5k+iLM4pM/77ImzxuThzsVzdOPBjgbc9eI/4H31O8hZ8/8UIXHSFQAPCRO5WmXuATYa26VJyrapYZvyus0vi091S+10reNgRJ5DLk/eF6uDWfX+hwtkcRgAexo8YJ2v1RYHlgLhoK6Dz5PFgUgy/U3H7CLLIuBLqVwzxkzpk/ljMfF46XtVCEUl2MwCvnf2OABno1kMAGaHxqVSnGVBDYtZHM4X9mrOdYvwNwiIifdMsjgAQGT8JWi0VmBESNWHwdeMy8WXMd60EWgHpC2/gXf+L3HC5cdYrlGxRjFwOMxGYYc0CZcKcnyDuWEb+E//C6jbjqzwcgD5GW1xAIBm5KECDQnnHZJGYRIviylzwzaAsbRxyw0GZHEYANX1sdxmaz9T2XoL42Iaj2IciGTjD8dbHPr2Gbab9cGHUtbIBLOHBo7jlK6bZoGHWeABi1P3hS8LhyKjUwAA7FwYFi5zhQMEC/5U9QouCz2iDOVwftxo+ruybd/zCgDZ4qB2U3jtoxCEFdukycqYpWYD+C8eB3/sMyxrehKANsYhXQtAGdElHNRlp+ORGId7IzELFR9sA+8eXgHrJBwGwNEW+R+HGSJ4xfYAIAW/tBi5KogUEoiIsKvTMfuQVQEAo/Lk+b+NXg0AkGz5CE69NnkLHETiXRXdIdnyZfGRYRRn23GEdS/sGGM46QpgkspNEcyVXRS72HhEDR4fkyIHkAMfnOr27BmUVZGvCAd9ZkUQcgn2P4oXYxebgFNSzCplch0ZnAWmCSQcBkBYlEuwaky8SI2rgoQDkUoCERHWAVgcrphRiny7GU9El+HX5c+j7caNYNbe+4nTieIsCwKw6Tp/GpFx1oZOirOsCMOMDuZIOKfVH4EvLGIyr+pNUTQFABCADXulcYbHLeS1KZqSvdBwXjqSZRUg8Bz+JmqD28OjLkDD97dh96Vv4bWCOwEAx1gsc07oGLx0/HSAYhwGgNhZRU7tpgD6/mutV/CxPxUJByLZ+OPrOPQxONJpMeFPN8/BkVYfzhyZC8Znrr+3OEsW/rWsSMkcAICw4IBF1ObsZ1IqppriLPnXcwvL1ZTOVlPbLAdIqi0O5hFTIXCAyIBt0mTM5PUPzG+pMmskay7QQ5fVdILjOJxXno8NR6dgcfRx3D/dh9kTRiMyZi6cJhuc2edg3JH92NMUwBE2EhdhNwBAGGYWBxIOA0BkXcJBa3FIhatCohgHIoUEdJUj+y5+c+1mzB6d2DecKRQ75YdqfIXFTWc8jG98/W8al46YPWZQ15YsijrfYwtyMQH1hnP8LXKDrzKuVRljeeNQlBVFoyeEHdIk3IyPdMddKsSyDCRncuvZDAaPLJmKTcddmFZ6Dkqy438WAuPyZSuNxuLQfhTDCXJVDABJ9lRov3B5C8Cl4LZqLA6UVUEkF306ZgqsZhlCjk3+txaJ+111xsU3YCV3nWYs3QJAe4tJ4FHg6L7Ykdh+EhZENC3HJccIFHSmLG5TpWQmQnKOGPhiBxmbWcD8SUUoyTb+AdhVE+QYK1PGhA4SDkQviTK9qyJlKWiqdEyBLA5EkgmERdhJOACQzdXnjMvDq+JCZcw/+0fIcdrwrVt+jtrsWcp4pExfEyFTmDUqFy3dCAfBXYNitGvGJGcxcm2ycKhFzwGkmWhx6IkZI+Vgz6OSSjh464dV6WlyVQwAySjGIRXxDYgrAAUSDv2FO74BqNkMvvyq0/JLrb/EN7lK1ec4U3hocSXer85BNStEuSOCwIzvwwJgysg8tF/3Gny7XkA0bzyiI6qGeqn95r6FE9H40Tgk8FTA4q9FMRdzPUnWPECw4uxxedh0wgWAw4fmRbgs8nHCa4gZaHHoiUKnBaNybajrKESImZUaPkL7MYjF03s8njGGXXVuFDgsGJOfOfEfakg4DICu4Eitbzg1FgeOgiMHhDsYwbubduKf914DnkWRVb4F7stXD/Wy0ob4AlAsgwLaUsGIbCtuOa8cwE2ILzzMLNnwz7lrCFaVXAqdFoycPiOhcMgKNqCEi7liJIfcxGz5rJGIiAwCz6Fq+q/x5ut52ObJxVx+N5YImzTnOF3F+YyROajtCOIYK8UUTu7jYWo/2ivh8OaOOvzm0yMQeA6vfG82JhZnXjpvRrsquJ1/HNLri0auilRUjYS2ABRZHPrOi1tq0PH1X5T4EOvxTwAmDe2i0gh/OC4dc5hbHIYL4XELwHjj3495kQZNW2nJKQsHm1nALeeOxf87ewwEZyHY3AfxhrgAByV9amomxjj0hpld7op+xDn85lM5A0OUGP7nk4PJX9wgkNHCQXjvTvDump4npgijdMyU+YYFquMwEF7aegreuHbCvDfBT61hBmNMb3EYxjEOwwlmy0d49FzDfSVSE0q4WIxDl8UhnrkTCpFrN+MgMxIOp6fFYWaZLBxOspgw4t2nEk1PyO56T8+T0pCMFg4AIHjTQTiktsEVADDerLwm4dA/JKatLSC0D6/c60SEohIkBk13zL72qiAyl9DEpYbjJXBhGndc2ZYcxYbzzAKPS6eW4BDT17SQsk5P4TCh2AmridcUCeODrn6dKypKsvUzg4IrM144QBy61ETFVcENQlaFKjhSAKVj9geHqgwuMPyKtiQiGJFdNnaoygSTcBg2hMcvUl5LjEOExb5ruhpZAd27Hb41oxTHmV4kSPbele7ONEw8hyKnBS7EhAMXbNdPZAyQYt/XUYnpphw7dRIFL52Hoj/MgvnEpylZb7LJeOHASeGeJ6WIrjoOBPOlagAAIABJREFU2hiHFH3hqvyQ6Z6Oub2mHdf8YSt++tc9sppOE+JLg5vI4gBAzqgAMKC22kTmwmx58Cz4DSI54/FbcRneEo1dF4lcFQBwXkUhRAj6HQniJ04HipwWtGksDm2a/VzIjfw3FqNw9Wxwx9YBANr9+udVwab/hOCtBRf1I2v9Q6lddJLIeOGgVnODjWHlyMHIqkjz4Mjb39iFE64APj/Sig/3NQ31cgDIXe8ciLM4DLNqb4mQhQPTuCrI4jC8CE67Du03rsdnJTdhgzTDcE4iVwUguyt+e+2ZqVpeWlKUZUE7y1K244WD/evfw9S6V+6e+cZ3Acj9P+KZ0hpLZzV1HE/NYpPMaSAc9H+IwUJJxxyMrIoMLQC1+UT//H7JJiJKsJOrwpBAWBycfitE2nPOuHx8IU3XxQMBPWdIXD6jDMHKZcq2mGPcBOt0odBhQZvGVeHSZGpZjv8jti8qJ/W2dVoceEh43Pw7rLf88yCtNrlkvHDghtLi0BUcOcAa/71Ba3FIH/N/PMGIVtTkOyxDtJIYjDEEI6LGhw8AgrcWiMRn6Q8/4stNA6DgyGHK2ePy4EIO9jD9Q787V0UX3vMfgpg9BpIlB54Fv0rFEtOGoiytq4JjEriQOzYhvhaKGEGbTxboi/mtuFrYgDF8s/7EGZAmnvHCYUgtDgZ1HFJWcU8d45DGrooGt/bhnGMdeh9nWGQQGTQllbsYbu1wjZDLTWv/buSqGJ6cUZaDikIH3hAX6PYxS89txpmjCG03foHWm7cjMvqCVCwxbSh0WOCBA1EWe4yq3RWMj/vR1FGjWByuEjYkPC8X6ki4L13IeOHADWVWheKqUBfOSVWvitQFRzLGsKOmA6dcA//1XesOarbDaRAcGei0gji4oG6fqXX/YC8n7YgvN804AVCl/xLDB4Hn8B/frMSr4kLUswJlXMwZC3C9bJXO8cPCYlWYZQEDj3bE4hw4VUpmvADg2o+j1ReBA0HM5XcnPC/vN7BCpBkZLxyGNDhyMAtAaSwOyX3PL22twW1v7MSyP2zFr/9xWOdu6Av1HdqHc2AA50oWXWuI/1UNAKaWPYO9nLTDFxY1cTow23v/kCBOO6aXZuOOCytwbfghpQlWoloPw5mu1uSuBLUceH9cYLjrONr8YVzM79C0Zo8nE4TD0NuRB8oQuiq6UnKtmsI5g2Bx6G2MQzQA66F3IWWP7tZsuP5IKwCAAXjz6zoEoyIeWlzZr2XWxQmHrhoBQ0lMOOj/sZJwAHyhqDbGYRj8WiS656ZzxuLCCYVoYRdCQAuiRWcM9ZLSjsJO4aAJkAzIroo6lw+F/hbNfK79ONr8U3GdqjaGEXygpdv96UDGWxzSIThyMFwVnNC3GIe99W4cf+fnyFl7L/LeuRamui0J53pC2nu4s9adYGbP1KtcFRfyu3Fe65+0AUNDQCDc5aowsDg0V8tFWoYx3nBcZ0wSDgSAiUVOFBaXIVo8gyxQBuTbzeA5GKZk/u6Tr/RB7K5jaPNHUKJqVb535r/ju+F/w26pPHaODLA4JE04rF69Gg888ICyXV1djWXLlqGqqgpXXXUVdu3aZXhcMBjEvffeizlz5mDevHl4++23+3bhNAuOHJysCtZt5K0nGMVNL3yFbzS8qoxlbfxFwvneOOHgDvZfjNV2WhwmcTV4yfxLXOd6Bs5Nv+z3+ZJBoNPqYTNwVfChdvDeusFeUlohWxxU92aYd8YkiN4g8BwKHPoiUBJjOFVzQjefc51Aqy8MJxeLJbNnF+FLaTr2S2Nj5/APA4uDKIpYtWoVfvWrWOqN1+vFihUrcOONN2L79u247rrr8OMf/9jw+McffxyBQADr16/HU089hV/+8pfYv7/3AWvpYHHQZlWkylURV5Wtm/e9u7YDzKf98JkbdyQUG96Q1oLhDkbA+vkrvL4zq+I+0+vgOfkc9uqX+nWuZNFVGTG+AFQXw91dYRjjQBBEjxTqyk67UO8OapqDKbiOoT0QRhZiVtmsbLlZVgtyY+cIDAOLw/33349t27Zh+fLlytjatWtRUVGBK6+8EhzHYfny5XjyySchSfoH15o1a/DDH/4QdrsdM2bMwJIlS/Duu+/2fgFpUABK06siVa6K+NKtUmJ3xaEmD2bx+uJGpqadurGoxJQHaxci04uJ3uAPi2gPyPeigOu+69u7uxvw4Hv7sK8x9d3huoI91QFJLSxHeW1qrk75GtIZL8U4EES/0JWdDrThaIsfxQbCgQt7kcc8cKqyu8z2HGRbTUoQKpAZFocBB0fed999KCkpwcqVK1FbWwsA2LdvH0aPHo27774bmzdvxoQJE/Dwww+D57U6paOjA62traioqFDGxo8fjw0bEue4xmO3cLDmOwb6NvqMJDF0/SZXf+k683LhSMF6snKzNNv5OWbAZnydw80+VPGHdOO59Z9BmqINkjSqnQ4AsJqQ38f30awSAQ5ogyTV56prD+C/PjkIxoBtNe3Y9MDFEPgU+lDNsrVGnVWxTZqMxcJX8njHviH5DKULIYlpYxzM9j7/7YcDgiB/f9G90TNc783IAgfaT8a+my3RDtT7IyiGgcUBQBnXCidiroqswiKU5rrR3BITDpZwW9rfxwFbHEpK9NXE3G433nvvPVx55ZVYv349FixYgB/96EeIRLTWgUBAvoF2e8w0arPZEAzq8+0TcbDehSt/txGvb+17L/SBIKpM+RpXxSDEOADo1lVxsNGLWZze4sAd/0w35kkQz9BuUFO9JxpVxZ+cSPw3PNLsU+IRXf4INhxOrcKWgyOZRjhskaYor7nDn4B/bTn4P14DeBtTupZ0xBNvcTCTxYEgekNhnMUB/lYcavIauyoAlHJt2u9GSxZKsq0aVwV86dHfpztSko5pNpsxe/ZsXHzxxQCAW2+9Fc8++yyOHj2KyspYmp/NJn9BBYNBOBwO3evesPFgPaqjbvz7O3tw4dhcOC2Dk2GqrnWgbnLlCQARV/L7qvsDWtdBu8sDFtJ/wTPGcKixA7P4w7p9orcVrri11TZ5Da9X0+zBGGffigCdUFsc4jIYXC0uJf6jtV27hj9uOoGZxc4+XasvtHYEYUEUJi7mKtsoTVdec1IU3BG5rnzw8yfhO//fUraWdMTtj2hSVZlg031OiNivabo3eobrvTEzpqnjwPxt2F/nxqUJhMM4rgkCF/vR2R4QkG8z4aDKVQFvM1xtvkHJZCku7rkaqBEpSccsLy+HxxN7iDDGIEmSLuAuLy8PBQUFOH78uDJ27NgxjB8/vtfXMqlSExP9ek4F6rbqg9FVMN7ikCgotMkbRm6oHjmcvgpkV6MVNfGpmF10BPp+L1t8KpdNnMWBC8cEii+sFUGfH26VS7GKqYlXCUREXUZFLStCo1Cmm+vY8UxK1pDO+OLTMc3pbSYliHQhy2aCK65y5PG2xBaHCZw2g4uZnSjOsmhiHDgpDC48tCnsPZES4bB48WIcO3YMH330kZJ1UVJSorE2dHH55ZfjySefhNfrRXV1NdasWYMlS5b0+lomVRVFcRDz8UVJ7apQfemmLDgyPqvCOHjxaKsPufAZnyOqdx/Ep2J24Q72/SHe2ikczIjqKqNx4ZiQVAuHHPhwBjuE1tdvQdGqSuR88E9Jr6sQiIi6jIoArNjDT9bNZRhe+eqMMXhD0bjW8OSqIIjekGURdI2urFEvRiBWQXKPFGsYVsHVK68ZxwMmO0ZkW+FCFsIs9h2f7iniKREOZWVleO6557Bq1SrMmTMHn376KZ5++mlw/7+9dw+Toyzz/r9PHfo8PedJMjkfSAIsSSZBEsJBEAQPKBgMoKyKQASR4AouQYX31Z+CuooI7LvK8rq87KKgu4sg4i64SwCRgxACuMEcyDmZHOc808eqen5/VHdXPVXVPT0zfZjpuT/XxUV3dXX30zWdrm/d9/e+70zopaOjA2+8YRrTbr75ZjQ2NuK8887DjTfeiK997Ws48cQTi34v1RZxSGvVEg7lr6qQZBm6fdQt9z7h7zoeQ8RjJgMAMI9JkKWMOGSFg5cxSLJFHGIZ4XCJ9BJe938RT/j/F1bHnwczUvDvfgZyj9vYORbiaV1InaS5jDQUbNIXuPZl4EK/+ZJTxfJhL1I6h2Zw8jgQxCioCyiuQVfNrB9TmTXs6i3D+p2ZL1mCgKthgDHMaQqBQ8I+bo0tl3vH9/C9khkC1q9fL9xfsWIF/v3f/91z382bN+duh0IhfPe73x31+yrMJhw8yj3LhRXd4I5URZmEAxg0yLk5FSxPxGF/b1yoaBjgQdRl0hbMSJknLlvaYzDl/Tp9o4g4ZFMVU5j7xMvS9lSF+RmuVX4HP3OfSKVET0nnf8ZShpDDj8NsFftyaq7nvwClexvS7atKuAIT/5ZHUffS/0aqfSX6L3rYHAZUZbJ/C7Eck/o4EEQx1PmV3KCrFpjphRPYQfhs56W3+HxcCdNDZU9hcNX0dc1rNlODu/k0LIApLOTeXRVZ/2ip/i/XGLFHHFJ65SMOQkUFTGNZOZAkQIMtXZHpX8E5xzud/Tl/R19cExqM2PsVAO50xaDDFyLBwGnsL0gPdWOkZCMObV7CwRVx4JhrC9sJpEtrsEpoOoK2YxKHKe7+R5sFQ4249pe7t5f0/fd2x3DDv76D6PN/C6bF4N+3Eer+F0v6HqMl26/Dnlri1ACKIIoikjHj2w2SJ0lW18gYC2Gn0e75XO4zf3saQz40BlXs4lNzjykkHMqLIqQqKhdxMDzaTZsLKlPEgTHognAwP/e9L+zGNY++hTX/9DriaR0DybQwPrrLXuYDAA6DpDNV8R3lZ/iV/9u448A1gi+hGApGHBwehxb0I2xLH+w3Wq19PUycYyGW0oUTYyIjHNJQ8O7SO9ATni/sr3RtK9l7b9rfi08+9AZ27BPLheX+fSV7j7HgHXGgVAVBFEMkYAoH+6Crk5glHPrUNhzhjZ7PzUYcAGBeSwi7uCUwKOJQZuzmyEqmKrRcxEE0AZbN48AYNNufi2U8Dj/fdAAA0BtP47E3D6I/oQkNRoQaY7hPynZz5IyoD59WNgIAGo0e+Lc/UfT6Emk9Z3r0Fg5iVcVsdjh3v4dHcBjWPy6mlTbi4DRHpph1YrzopZno6Po2/lf6c7ltcnfphMMjb5h/n4XsgLCdj5N0QPbvL3ocxsfaCGK8U+f3ijjsyd0e8rfhKIYXDvObw9htWBEHEg5lpuqpCuaIOJRNOMARcXB7A44NptCXEFMVgwgixq01OQ2S9ojD0jqxBEjudTeRyoe9FHOKZ7tVK+IQS2mYw6xGS3v5FMQLrHGsOMsxvULx2/nM3G15BBGH0J/uRv1vroRyZLPn49mBYQslUThIVZ4YmmUok6qg6ZgEMXL8igRVZuixTciczrpyt1OhaUhDcaWMAYDb0qTzWkLYza3ycCnRXV6T9hiZ8MKheqkK8/+uiooyNe2QJebwOLjtgyndQH9CTFUM8UDODAh4RBxs5sgO/2HhMblvT9Hr67IJh+myd5/23JpSOmZJonCIwTpZsRJ7HOJpQ6iqaG9txuxGUTzsNKx/tHKyR1hvPtQDf0T49Xvg2/8Cov95nec+2UZhJzgiDiNNA5WLbJSIyjEJYnTU+RVh0JUdI2KmH7zSFdxnS1U0h3EM9ejn1u9S6lhpq8tKSWXaLJYRsaqi8hGHQAVGagOmHtHtOs8j4pBI6xhM6ogoNuGAQMYMaJ6onMLhhMHXcaX6LFRoeP8xsQRoJMOfumwtqtukfjhH0Ts9DvaIwx4+BTOZNRGuHKkKe1VFKFyHn39sBf797U4cH0zhk8va8as390HbIuW6S7KhIznzUj78O6xhbHKeuutERswuZAeF7eNFOGRTFUFQAyiCGA0Rv4LupLdwkOqnAwAO8yacDHHUtuBxaA4BYNjNp2EpM9MU7b9Zg55Ln4Q2dUV5Fj4GJrxwUO0eB72C5ZgeHody+RsAQGYMGpeR7U/EPbosZq/6nRGHBPflnmcXDizZhzti30VIzuzvzLoMHQaLd4EHm4dd3/FB6zg0oc/1eOjPD4EZaQyd/nUMJTXMtgmHfcYUtErWc0ofcdCFORVQQ/ArEj69YkZu02XLZ+HYlgZMg1lNovUfgtQ43/lSDoYXqrmIg+SMOJQ2VfHnzn48s/UoLlzchlPa3WHRfGQjDmSOJIjREfEr6IX3RYa/0fyNOcIbXI/ZhUN9UMXpcxqx++BULIXlb4j+/iZ0//VLFWk/PRJqK1VRSeGQqarw2fsQyL48e48d0xw5vMcBgOBxGEJQSFXYqyrkvr2uKZZOio06dGWmbDIYiBpu4QAAwS2PIPzytxFL66JwwBTEYPM4lLCqQtMNpHUuiCn43FfUrRGf8I872VtE5zbuSBfpSdcuSc1AE/rRwkShwJKlizhouoENT72LX27uxN/+5t0R/TvIVVUI5ZgkHAiiWOr8ssuEniXcPAsAcJC3uh5zRjR/ePHJ2NPyAWGb3L8XLOn9e1pNJrxwqLY50h7x4M4JliVEYmIfB0N3C4esQdFeVWGlKkyEk3LauzW1HeW4t3BQ9/8BDf9+CUKv3wP/tn/DX/9lHdbKzyOKGGRnnsJG8N1HEU0dRSOzPARHlXZBOKCE5sh42lzLcKF4VZbQxZpy9/X+PD0mbDBHa2yWcP8DT2gGTnCkKQBAKmGq4mBfIicau4ZSODLgFjD5yPZxoAZQBDE66vyKUFVhR66fjraID/u5e4o0d/SQ8SkSpp16KdYkvylsH4/tpylVMUqyfRzsEQ9IZYw4SGI5JveIOGTDzp6pigwsbT2WTgxvAPTsaWDoaPjNpwAA6mGzdXgUwA/Uv2CfMcW9v4PrZcsbYCghDBlNiNsmfZbS4xDLpAqcqQoveuXmnDeDDxz23EfA0UxLSvZBD1s/EAbnSGoGmiW3oChlqqLHMQLdOUSsEFYfB9trUMSBIIom4leww8McafjrAV8YS9qj2LvDQzj43BOBWyJ+vMkXYrcxBXMzBnJ58BD0lpNKv/AxMOEjDmKqwjviwDnPnehLheYRcUBZIw5iqsJLOGQRUxX5Iw7J2PBXvV4mPuXoW3n3/5j88rCv+Vnl97nbWnQWQn5FTFWU0OOQ7agZZIXLMQFgUG3J3ZZjR1yPS3170fjYBWj45YWQBjshOcqlnCHFVMYYGfaYHTJac2RaN/DCe13Y1WVFi44Nib1ERjKgbCilQ4IhlhUrZI4kiGKJ+BWhHDOL3jAPAHBKexT7PCMOHsIhbF7kHeaWr0waHD76WWkmvnBg9lSFO+JwfCiFKx7ehIsf/BO2HRn+CrtYsqkKu3Dhslqy13ciOaoquK65xpRnKbYcc2jIuurdxmdBr5sJAwx/MWZ67p/Fv/vZvOv8sLJpmE8iwqMzEFRlh8ehdMIhO3MjCttreih9AIj5rDykGj/qejy45V+gdL0L9fgW1P3+JledtZQUy1ATmTSJczInMHqPw4Ov7MVXn9yCTz28CQd6zb/NsUHx9ftHMF5+IKG5mphRxIEgiqfO7x50BQCpGWcCAJa0R9GDOqHUEnCnKgCgOSMcOmGlTcdjqmLCCwcVGloyLn6vVMWPn9+JXV0xHB5I4uYnii8vHI5sBKOaEYd81acuc6StuZLdHHnomNWopM83Bd1XvoivTPsFfqJ9PLfdSzj4dv/etS1Ls20yppeT2IkenYWQKjnSKSUUDnFTOCyw+wwa53rumwxaVwXBpFs4hDb/NHfb1/kqpPhx4XHmFA6aKSrDHgZUaZSpiodeM1tXR/kAXnz5eYDznL8hS1+RwiGW0rH16KDobwCoqoIgRkAkM+hK8GkBSM88CwCwqC0CgLl8Dl6pCr8ioc6v4JAt4iAPUcSh5ExhvXgj8EX8vXqvZ6ri+fesk+NRxw/sWMhqFNUW8eBSOSMOmXLM3ALSwmhvCy5USqTlIBJ5Ig5He6wr5mA4CsgqtFCbuL/DqCj17YHSU9wQqL18eL+DUTcTQZ8j4lBCc2RfQkMQCcySrD4RvNV7bLtmEw6R1HHAEdFJt5ws3JeHRB+E5DBHZiMO9jRJFqbFAY+S2kIkM6mPVvTiRf9X8OXd6xDa9PfuiENGLO3viedGmHvx0q4uJDUDiyVxjga1nCaI4on4zd/lKBN/t9KZ/guqbJ5mnb+HXhEHwExX2IWDNEARh7Jxkfwa6mN7XNsVqTz1r7qnObK8qQox4qDbRntb+JHONTECAE0Jix6HzNW8phvo7bNOdPV1ZnQg4pOF/Z1DsdSjbxe95r1FGCX16EyEVFnsHFnCcsy+eFqoauBqCGiY5bkvr7N6xas85fIssAK+EsDtccie6L0iDoA4ajyLphvojnkL3L3d5t/uZuVfEWXm7fBr33dFHPoTGh598yDW/NPrWPPgH9FzzF3VAQC/33YM18lP4VHfnbltXFLKGjkjiFojO6/Cha2vz2dXzXL5HAwPjwMANIdVHOKUqqgYTYm9rm1yuYSDl8ehjD+4ssRcHgeviEME4klXl4OIc3fE4S9HBuEzrJRAY705RTPsVzz3zyLFjqFYiok46HUzEVBlcVZFCT0O/QlNnBPRshBg3l97X7gZKVtURxoSDZJSvMv5FIF8qYp8vTKYY15FPK3j0ofewId+8ir+7S33j8WuLvO4rJT+Imx3Rhz6Emn8aONO1CGGp/iNWPCr0+H/yy+FfYZSGl7e3YWvqY+Kb0LRBoIYEVnh8K/a2bltQ++7Wdjn5g8uxNT22cI2L3MkYPochFTFYKcr+lltako4RFNuJ7xcpo5b2ZO2D5VpAMU8GkBpHsLBbozkYIAa8qyq2LS/VzDtSX4zbOaMOIxFOByF6HHYYUx37WPkIg7lqaroS6SFyZT50hQAUB/0CZPsJHtlBTfAEt0F30tKeqcqvKoqAHcvh1+/cwidfQlwAD/8b6sMVtMNgBvYdcwUGtOZ6K2wRxwYDPTHzL/rl5Qn0c66IcFA9LlbhOe8vrcXcwxHigKgHg4EMUICqnka/Qf9YvxB/yscn3MxYituFPaJ+BV87PQlwrZ8Le2bwz502oQD05Oui5JqU1PCoSXp/iEsV8TB6uNQGXOk7JiOyY18EQebcFBD8KmKp8fhQF9COKHxTG+DsN+5v3jSYzHxpFWIZSfMxx51Qe7+j7RPCo/HWAjcH3V7HLQ4wEvTk6M/oTmEw6K8+9YHFWEYjTRoeRhYohdsmDU5UxVWxMG7IZOzl8OeTCriSvm/8D/+a1D328/h3o07cP39jyD4wCn40ruXYQ47BD9zpEwyf9NT2C5s8l+Puw59Hu04juVSfi/Ka3u6cZb0Z/cDkuzeRhBEXrKVELv5NHwm/XUkL7zf8yKSty8TN+QR6S1hH/oQFqYajzefQ00Jh7bUPte2cgkHrcKpCsaYa8iVl3AICcIhgoAipgGQaQCVSOuOfc2wWcTnSBvoSWESpxQvPuLw4RUnof6S+7Cj9UP4m9QN2Gg4/uFkokEhVUaCO+Z8aIVbYRdLXzwtzokoEHGIBlRB6dungzorKLyQEt7lmPkiDs6SzJBqfn/uVP8JQZZCYO9/o/etx/E9+SeI6H1o0Y/iO8o/uV6nEaZX4m+VX6KJDaKdH8Gl8ovC5FYAuXCnfGwLvrLjU7hDfcS9poHx5+AmiPFMa8SPa1fNQlvEh6+eOx8+Jc9ptfkEDK1YDz08FQNn35l3/kRLxAeACT6HfEP0qkVNuaCmaAdcY4fK7XEQZlWU0RwJiBEHGJpnU6uIPYrgC8OvSp59HJKaOGo6KxzCfkWcbQGYJ/FM6ZA0goiDEWyGUT8bkU8+iKl/2oc1CQ3YYj0uG+aJzRlxAMx0BS/BlMZUvB/TmeVNKJiqCCh425iB7GFWuq10wXD+BqBQxCGPcHCkKgaTGnyOk/3F8ss40Vb1cKa8BU4a2QASXMVqyXpsJjvmEg4s1Q/urwc2PYjp3J3WIwhidFx3xhxcd8acYfeLrdqA2KoNBfdpDmWbQDVhPkwh7/RbVZuaEg4t+jEcS8eElsLOqgqDc0gl8D14tZwuZwMoANDY8B4H+5wKroYRUCRPz0JSMxwRB/OYuaoqMs/J1hwXijikI9OhDloOfiNodmJUJIZrVmWMQbbz3lF5CgIAQg5xk3vPvO9UPOGElW7QJR8QdfssstQHVWzj1sRMqcsK9bPRCIf0MFUVjlRFVyyFKUz0UXxQHr6hVgMbxBJpl1BNM411IcKc/pTj0P31GDq2x9ZeRoRHhje0EgRRPsyIA9ALyzzJUuNr0FVNpSoAQO7dLd53CIdsidxY8aqqKHfEwXBEHCyDZhpfU36OH6t/7yg9NIWDl8choRniCc3mcUhC/Bw5gyTnkGL5T6DarLPFDR4Rg+fnWCa9g6feDgAIqjI4JLGaowQGSc45QklL6KSDbQXH09b5FbxnEw5K/56cfyDVZyl+ewjRjpTsE9zP2e+aPbIjlFk5Ig7dsTTaUdiA6UUjBnGR9IqwbQY7hilwtMSOHcWjbx7EUK949RI75fPQ62aASyqM878z4vcnCKJ0RDNVGgPc+v2USjhNtxTUVMQBAJTendBbrUY92YjDR6VX8Wn5vyFv/QJwyqVjfp9srym1Qh4HwJmqsPo4nC9twnXK0+Z22y5cDcOvyDjO80QcPFIVEZ91Eg9mRi1nn8OSfWBG/iZa8WXXwbfnOcixI0jO+5DnSXrxhTfipVfboQTqMG/5hwCYwgEAYvDnpliWoiQznjbQYjsRG+HCV9OyxNDtn44kV+BnGhg4eNcOfHuzHydsfwc3ZY7tn4zFuNhjJgfTk2ZaJ1PS6NU58ghvxLRMVMFZjtk9lMIJbPjIhpMF7CBOl94VtmUH5Ng5frQT976g4QrV+hHafubfo3HpJRg661tgqQE0TJ024vcnCKJ0hLPCAZZwGO1sm3JRc8JB7nZPc/Qjhf/ju8+88+KXcWzRh/LOKygWr7HaZY84MO+qihnMO33AfREEJIfHIZ1YS/B3AAAgAElEQVQVDrpwQssJh8yXNg6f7SRuPsduEExzGQaY4PDXozPRu/YpKEffRmrWOZ5rkhUVi868XNgW8smZ9/QDGMisc+zCoS+RRpvtqpvVDX9SDAf82BmbjpOY2RPkNxufx1MHl+HbihUqPFqglbaU7IWRFQ5pAwAXJnMe5k0Adprrsf0YcM7RHUuhnY084nCFshEyGz6xc6BzHwyjGU2wBEvLrFNM6csk0/9AEERVCSgSZCZGHEo5TbcU1FyqQj34qnBf0zkaIaq1+me+gKZ/Ph3+HU+O+n2sVEV1zJHMJhzyBd/NiIPT4xBD4y/OxazUe54eh+zVv5cvwt7DoQtRt1dE9sOItCM178Mj6geQe0+PyMhY6I9rmMIs4cAjUwvsbVIfULGdWz6I9BGz2VIzs/7hdvEoNqTXZdbs8GbYfA4JzYAPmtCW3D6/w77vUEpHSueYlifi0D/93Lxrnlak2Og73okoYoIXwgg2F3gGQRCVhjGGsF/BAKzf0PEWcag94XDkTcA26yClG2IZIwDfvhcgD+xH9Nkvjfp9rCFXlUtVCBEHrnv7LGxwf71pjnSc3JSeHfh86jHhBJKNOMgSQ9gnC0OnssfTXlFxnNeDlai5VsiWqshSiohDbyKNqTbhYISGN/41hVRst00HPSHTA0IQDojil/q5ODd5N05P3o/D9t4PduHgKHkFgJ28PXeb2fw4XZnR2F7CYdPMq8GXXClse0pf5bl+e+dLJ5cMPoqvKb/I3eeSQlEGghiHhH2ymKoYZx6HmhMOzEhDPfxG7n5aN4STu4th5g/kI1vRoNrLMctcVWE3RzI97dlLAgDSUgA82ITEwk8goMpIOKokAOBsvCHct7c/DefpHhnrsyoUjvN6lKrSNejziHKUIlURTwsRh+E8DgDwiSXTsFuyhMM8ZpZDNdvC+yxkVos0Tl+MXtRhiNumSdrW7TKgAniHz8/dVnt35npkdMfM0kl79OBB7aN47IR7MeOibyI9fTWMgClQ3pJPwRuGu5HVEdaKTR7b7VyhPJ+7bQSaC5pFCYKoDmGfIpojKVVRftSDlnEtqRlQWH7hUEx9vhee5ZhlTlVozBbR4HpurLZs+3y/1M7Bf3/8Nejr34beclImVTF8K2xuSy04ezlkhcPxI1YjpUGlsfQRhzzjv0dLf0JD2wiFw1nzm3H7pefk7rcxs6mTPeJw28dX4bkvrcZXP2CKgKT9WOmWUEikddGAqgRwLDAfBjePm6QnIfebXgqviMMZF/41zrvgUkiSBO6PoueTT6H/vB/j3sbb0cPd7Wo3BU5HJ4pPPfBgvqJMgiCqScQvo38cmyNrRjjo3DqJ+WzCIa3zvKF8YGQtlIX3M9ypinJ7HHiezpH2NeiQMKUxmjN/BhQJ2jAeWEMOCK2GXd0jMyfxgW6rq6AvOgXxv/ps7n566qmj+EQmwUyv9xhKO+iqL55EG6xujkZ4eI8DAAQaLBNlhCXQih40MmuSJQ+1oS6gwJcZl5uwla8yzRIKSUfEgathhMMRYUqe3G32iuiOpeBHCs3M+oGYMn2esC6jfg6Siz8JKdSIHtS51p2edprQ+XI4sn02CIIYX5gRB/I4lJ0/GNYAEbl7R+52SjeGEQ5HR/V+lZ6OCQCGLeJgN0fKsLwKaciYGrVC5wF1+NkDzvGu+eZV2M2R0aZpiL3vK0jNPBvptqUY+MAPR/hpLBRZgiozUayUIFWx58B+0QhYRMQBME+o3DZB8/3yO9ZjvjoYdaZPwZ9pLZvk3rM9EpohDh1Tw2gO+7DD3isiIxy6Ymkh2sDBYITEMbxZogHFM+Jw2ukfxMkLC6cq7JAxkiDGJxG/7C7HHEcTMmtGONiHEzHdvOozOIdm8IIeB2Ow+NkLdqw+DpUsx7T9ubgOjbtLQnXIuaErgHVyK4SztbNrQmY6DmgJLNasXgHBllng/ij6Pv4L9K59GnrjAowF94TMsaUqjgwkceDAntx9TQ7lnUbnQpLBA9ZJ9TNNVgdJrfmk3FjubE96oWGWbkUcEmlnyWsITWGfULWRLR/uHkoJ/gYj3JbXM1PnV9HriDjooSlQGqZjxYliS+3BVbfl/ZgkHAhifOL0ODBugKWHqrgikZoRDkJ7Tj0JcI5UpnOfUDLpZGiUwsEjTVDultP2qgpm6J4RBw0yJJtrcVom+vCnQqY5j4iDUImhxYEd/4l6mF/cGPdDXXDeqD+HF/VB1VU2OhZ+9+4RtNo7J9YVl6bIooetq/1TEq/nbmu25mJWqsLucbAJB1db7zCaQz5sN+wRh4xwiKXRWmRaJeyTXREHvdH0W2jNJ5rj1GGKifiSa/K+DqdUBUGMS5xVFcD46uVQM8KhjzsaOhlppPXC5YoAgNjYhIPCKjNWGxBTFeBpz3SJBjE1ceKUCL505hx8M/05PKPn8SE4mmF5VVWo7z6Wu/8fxko0N5bWWFfnVxCzVSeMRV1rBscTfz484ooKOzzUmrstpS1/g9byV7nbfo+Ig5CqSOsIC+bIEKbUiakKuWcXYGjojafRZPM3FIoGHB1MYggBYZveYPohjLrpGPjAD5FY8DH0f+RngBpEb2iO5+sYZI4kiHFJxK8gDQUJbvttGUclmTUjHPohnvyYnkRSN6/E1UJVFaMUDl59HCo5q8IecbALh5OmiV0NGWO4auUsTF/4PlyXvhlJ7l4jVxypCofHQe7fh/rDf8zd3xj8YEkGhdmJBhQMlaIck3PseO5nuGzoESxi1lTJYno42NHz7K+1WsIh285c6Hmhi+bIoCNVsaA1jJ28PWfmZUYKct8eDCQ1NNkqN3gg/0n9Aye0AGB4z7B6QiQWfdJ63xMvx8CFP4E2xRxjvufUb2GjvlSsWgGZIwlivBLOlKiP17bTNdNyutdpFtNTSOvmSbJQxMHeRnkkaB4tp8udqtDzmSNtwmjJTO8r1ewXsR8htEKctOb0ODSHfRiynWT0fa+AZWZVHuaNONawfAyfwptoQEHcdhU92s6R0oFXcPa2b+JsxzdbbzphRK9jjzjktkk+wcvBGINfkfJHHDQDYYjzQBa2RpCED3v5FMxjZl8MuXsbBhLNaEJxEYdTZzXg8o52/LzzBvyN8jiUBedDm5a/qqVp8Tm45FkVl8kb8Xfqg9Z7hEg4EMR4JNv6v5+H0MrM3+vx1MuhZoRDnzPioCWR1MxylkIeByV+HPnHNuXHq49DJWdVMG4NubJHPfyqd8+G7DwI+xcxC3d4HOY3h/CaLeIQ0q0v7F+MWWhvKL6ddLHUB1RBrIw2VXFoy0bPTgaJEy4Z0esYHsJBa1oIyOLx9ckSkrpNOGT6OBicm4PEFNHjEPErmNkQwI6hGZgHUzgo3dsxkHwfGu2pioBl9nUiMYavfmABgAVI4aphv79BVUZ7fQD7BsQoilEgqkEQRPWwIg7jsySzZlIVgzwo9HKAnkRaz5oj84/SVhNj6+NQyXJMzuyjL+3mSPsavMsvIz73xLXccxzCYV5zWPA42NnOZ+QMl6WkLqAghrF7HPqH3M9LTT8dRv3sEb2O7uGJyIb+7fgcY8uR6eOQNeY6qyoAYFFbBNttPgd2fBuSmiH0cCh1c6Z5zSHsMsQhX15RFYIgqk/YnxEOfHy2na4Z4RCH39HBL4VUEeZINdmTa/s7ErInbV8FyzE58x5yJQijPGuwRxxcr+tIVdQFFKh+934AsN2Yien1pRcOpsfBLhxG53EwYu6BT4nFl3vsWRivk2p6+mrXNr/MBN9INlVhTsaEq48DACxsiwiVFdkmUPZhbEagtKWSC1rCOIpG/B/t44izEIZO/Zviy1MJgqgoEc/R2uMnVVFDwsGHlC3zwvRk7qpPLZCqYDDAEj15H89Hto+DvZ11uVtOCw2gbKkKpYiIg93j4Hpdj0ZD9XVRz9fZzmegvQzCoT6glqSqQk72Cve15hORnP/REb+O7nFMUtNPd21zRhyy5ZgJzfybOD0OALB4ihhxUPt3Q4HmqKoobcTh0qXT0Bbx4UH1M3jtE68jtvKrJX19giBKRzgbIeZkjiwrce5HSmjEk0Iql6ooHFGQ4segj9Ao5j1Wu9ypCkvniREHu8/Cew2FIg56dKZrW2N9FA4PJQDgPd5etlRFKSIOvpQlHHbP/CTqLvz/AHXkngynmDICjZ5RCFUWzZHIRBwGk+bfJNv7ArCEw6K2CHbzadC4BIUZkIw0/uS/AU32ttYl9h9MjQbw1BdWQjc4VLlmrhcIoibx8jj09HTlSSBXnpr5BUnAJ4aM7R6HAuWYgDguulg8yzHL3gDKHnHQcpUdchHCIatgnWWrAGB4CIfW+gbXtr1GG+IIoClU+s9ZH1DcEzlHmELSDI6wbqkdbfY54H7vyMmwOHpb5OuM6c8TcXjv+BAAjsXSPus16ucAAJpCPqi+APZwq8mTXTQA5emxIDFGooEgJgC5VIXtQu+9g4fy7V5xauZXJA5/gVTFcBGHkU/I1L3KMcveAMpKQ0g2c6S9TwVn+YSD22yTRa9zC4cpzW7hsIPPwPVnzC7ZVEw7UUdVBTDyksxjg0mhMiHaOLLeDU4Mf33udmLhGs99fLLkEqwAsO3oINrRJQzHsnedbKvzC+kKO5xJ4Lb3JghicuGTGRSJCR6HdMwjBFwlakY4pCEL5kgI5sgCLacxup4BmmeaYPjx1WPBLkyYfay2YI4cJlXh8DjEpbDnSWpqk1s4nLbidFyzamTVCcUSdVRVACP3OXT2xgWDoRoZm8Fw8Ow7YfiiSM46F4mTPuW5j0/xTlVsPTqIk6U9uc163QxwW4nllDo/ntTdZksA5n6sZv5pEgQxQhhjiPgVIVURZWMf/FcqSvrr9NBDD+G226yhOjfddBOWLFmCjo4OdHR0YM0a76u2jRs34uSTT87t19HRgXfeecdz3/wwR8QhhZSmYQXbJuSZPRmFcPBKVVSyHJNxbUQ+i2x5j9Pj0KNOBTwiCIGg23Gvz79wxGsulmhAQRIqNG7zcYxQOBzt7obPFn0Za5+C5MJL0HXt/6D/Y/+S97j6ZclRzZME5xzbHcJBazlZeN6UOj+eMU7Dhcnv4QAX/TXUX4EgiFhKE36vp6AH3MjfWqCSlORMp+s6fvazn+Gee+7BxRdfnNu+detWPPbYYzjppJMKPn/r1q341Kc+hdtvv31M63BOKTx92524xv/ksM8bTcRBNzgkGJCYbdRpJcsxue5Zjpm3qkL17uPQ62uHZzW/w1CoR2dDm9IxilUXhypLCKoyYgggClNZFzJIbj0ygKRmYEl7NJc66e8+IuzDA+6oyYgZ5srfp0gYdHSOPDyQRH9Cw8nqntx2rfUU4XlTImZaZhufhWf09+Ea5T9yj9EMCYIgUjrHdmalkedIR3Bs/x+B2WdVcVUmJYk4bNiwAZs2bcLatWtz24aGhtDZ2YkFC4Yft7x161YsXrx4zOtw5ppPOjK8aABGN8JZN7irzLP80zEtnScZWm6stlxE98p8VRUDgWleu4MronBInHCxZ2SilEQDqlBZ4d/+a6idr7n2e31fDz7zyGZc+9jb+K/tprF1T1cMz7xljb+Oy5GyV7kAZi7SPquCaUlsO2L6GoSIg23GBWBGHLI4vQ6lrqggCGJisptPw4u6ddERfOuBKq7GoiS/rLfeeiva2tpw//334+DBgwCA7du3IxQKYd26ddi2bRsWL16MO+64A/Pnz3c9f+vWrRgcHMSPf/xjhMNhfOELX8Cll1464nXYUxUhHy+wp0hQ0eBv9G54lA8mSy7h0NAUBYIje52RoPisE5QEHWpGDNjTJZFoGFLGOd/o+EyqzDBgiNuSdbNc+wEADD94ZArY4BFwJsN/5vXw15XvswFAU9iHWNKPzFRohN56AKG3HoD2maeA2Wfk9vvfD1hi4uu//QvOP2Uarnp0M061GSM1X6Pn55LzHJvRUhfyibMqjCT2DiTRiH60M6sZVXj+qQhHrfec325Ve+wwpguvqda3lmx9I6XUx6eWoGOTHzo2hRnN8Vl35lw8+NJuPKh/FGfLfwYARA48jwCOAY3l8ZoVS0kiDm1t7mY58XgcS5cuxR133IEXXngBy5cvxw033IB0Oi3sZxgGpk6dio997GN47rnn8L3vfQ/f//738dpr7ivN4RD6OGjJ/Ds6GUXPAN3g7v4QVaqqKKYcEzBLfPod48dT+aZGSjL0T/xfGKdcDuOKx4C6qd77lZBoUEXMo1JZfmq9cP/YoPi3/dOebgwldTTYjJFypDIDnPyq7DJHHu5LYJpNNHBfBKgTIzvTbE20djirK0by3SUIoia58dz5uP0ji/EH4xQc5JbRm3VtL/CsylC2M93q1auxerXlGl+/fj0efvhh7Ny5U0hLSJKEhx9+OHe/o6MjJyJWrlw5ove0/4Ane4979Ej0JjU0gIGekYmHVFp3RRx6+tOAUj7nq2bYG0DpGIqZIsxejjkwpCGS6V/R4/hMQVVGP8QURJzVufbLEe0Azs74GkZ4fEZDSJFclRUAwHr35F8jgHf3m02f7J0X5VCT53Oyir/Q640EruliqkJPobs/jnpmGTsNfwN6esV0WMCwImJO30l6oAv9FTjeXpT6+NQSdGzyQ8emMKM9Phef2IYHXtyF46l6TGdm24Ch7i4kS3ScW1vrRvW8stV8bdy4EU8//XTuvq7r0HUdfr94RXn06FH88Ic/BOfWD2kqlXLtVwwpu8fB0Xq4IKP2ODgjDhX0ONiqKoopxwTMXg6DCOJdwwxzHeTN6G1aWp7FjoJoQMEQH74rpSqLXou93eY/ogah82L+6ZKlxOeoqgCAZDKWM3gCAPe5m1CFfDLq/Nbf6hndGosdX3JNGVZKEMREJKTKGOS2KZnpwQJ7V4ayRRzS6TTuvPNOLF68GDNnzsQ999yDhQsXYu7cucJ+9fX1ePzxx9HS0oLPfvazePPNN/G73/0Ojz766IjfU8g1J4sfCDLaPg7CnAomAXkqGkqGM1XhOauisHAAGK5JfRUXyq/jBWMprveVvn30aKn36OXghSpJSOvWZ97bE8f18m/wZeXXuW2VKmk0Z1WIgjGViCMqRBy8u1dOqfNjIGlGrb6V/ix6eQTvW7IUdTPOLN+CCYKYUIT9CgYH7eO1qy8cyhZxuOCCC3D11Vfj6quvxsqVK7Fjxw7ce++9AIDOzk50dHSgs7MTfr8fP/3pT/G73/0OK1aswNe//nXceeedWLhwYdHv9cIJdwAQzZFSsvguW6MRDgZ3VFWUOdoAiKJAgg4jc/IsZlYFYLWdPoRm/D/9Q9jNp8GvllnsjICTptbljzikrBOxM+IgdW/HbepjwrZqRhxSqTii9hkVebpAttVZz+tECzZoX0DPshvLXr1CEMTEIeSTMWif4zMOhl2VNOKwfr1oYrv22mtx7bXXuvZrb2/H5s2bc/eXLFmCX/3qVyN+P+36VzBw9DAWta3Aqb1/RurQ6FIVo+3jEKhg8ycA0JhDnBimx0EW1pFfwGRLMu0ElPHTofCcBS34oz8Mrw7h8sB+6M2mN0ZxzFuYkj4Ix7kbRrBCwkGRkIYMnTPImZ4eWjImeBzyCYeZDUEA4mTWOv/4EXIEQVSfsDNVUcsRh4rQsgjatPdBliX8zfvnC30cpMQIhMMoqioM7uzYWP6Ig2uIVkY4CF4Llv/E4yUc/ONIOMgSw6IZ7godAJAHDuRuS44LcrspMosRHFu76WLxyxIAJkQd9HRC8DgYeYTDJUvcPTTsvgeCIIhQxpuWZTxEHMbPWWOMhP2yUI7JRpSqSIz4/TSnObICwsFwzMJgegpA8eWY4XEuHABg9hTPPpaQ+q0pk/G0GJJohuhn0SPTka6QT8CXOX52f41qpIS+8vkmdC5oCeMzp1qlmPUBxRVNIQhichP2yRi0Ne4baSv+clAzv1IBRRI8DhiBcBjNrApn50gul/9K0ZkOYboZcRBbTo9MOASU8RUaZz732G8AkPv3AwA454inssKBowV9wkTM5Jzz0f3pjeC+0ZUZjZSscLCP1vYjJcxH8aqqyHLDWXNx/sJWBBQJV6+aVb6FEgQxIQn5xGFX0jiIONRMXNSviI14JBTfOXK05kh7VUW5J2MCAHd4HJiRggTZMS8j/5+0JSyuUWJmCeR4gqt5hMOAKRySmoHM0FP8g3ovPiL/SdhPazkZUCvXvc6XMWomuZrreOlnabGqIpB/RLYiMXz3YyeWdY0EQUxcwj4Zh7ndHEkeh5Lhd443HgGjNUdWcjImAEiyjBS3lWQamkf3yvwRhAsWt2HZdPPq169IuGrlLNQHK+DNGAH5hAPrM1MVsUyaYgY76hINQOW8DVl8sjviEHBFHPILB4IgiEKEx6HHYXxdbo4BVWZCA6jheEJfjUvklwEAzNAAPe02HxbAbDk9/DjrUsIApKHAlxELzEhDhugULFRVEfErePCKZbmR4NI4LPvjeaIFcs97gJZALGWuvQHeqrvSA6L8Hh4HP9KCxyGfOZIgCGI43OZIijiUDMYYDLm4dMGh6R/GD7XLxeePMOqgc2fEofxX7rLEkLZpPWak3d0rC1RVZJEYG5eiASggHIwU1CNvIpbxNzQy7388FY84eAiHAFJi58g85kiCIIjhCPtkDIyzzpE1IxwAAEUIh/7zfoxtK+/GMS5eBTJtZCWZrrHaZR6pDZjiyC4cJJ4SKypQGQFTTvKlKgBAPfByrqKiEd7huqqlKmzzKiIsjhCzBlWRcCAIYrR4Rhx48R6+clBTwsGQiphvISnwZfwQBrdddY9wXoXZx6Gy5ZgSc3THNNIj8jhMBJzC4Tf66bnbysGXMZSJODQz75bileoYmcUrVdHKxIoew99Q0TURBFE7hH2KWI7JdWAULQRKSU0JByjDRxy4pEDNNO2xj3AeaarCNauiAh4HmTGkuFM4GOJOFVhHOeFM/Er+p/6+3G31yGYk42aYzqvpEwAYwcp6HFQ5Kxys714brOZjXFIAJeh6HkEQRDE4W04D1TdI1pZwkIuJOKi5q8S47cd+xB4Hg8NX7VSFkYbMRu5xGM8Y0VnQ62YCALTGBXhZWYnBTCmSZKQR6XobANDkkaow1Ehx34ES4pfdEYc2ZrWR5v56mj1BEMSoCftkJOCDxq3TtVRln0NtCQeluFSFmstL2yMOxYd+DM+plJUwR8IlHFwloRP9JCXJ6Pv4zzG4+nb0ffT/IRoOYQufk3s4MGiWZTZ5pCp4hf0NgM0cye3CwYo4GAWaPxEEQQxHKDPVeDyVZNaUcGBFXG1yWc1dJY424qAbWeFQ2XJMyRFxkJ3myAmepsiiN8xDvON6GPVz0BRS0cktQeCPHwGQZz5FoPJeAq/OkXbhQMZIgiDGQigz1Xg8lWTWlnBQi404mFflcbvHYQTmyKxw8FU44iAxMSQuGZoYcWC1IRzsNIV8OMwt30IwkREOHqmKbAvuSqJIDDLLb47kZIwkCGIMKBKDX5HEkkwSDqVDKiJVwSXV8yoRIyjH1LlXxKESwoEhbescKfO0I+Iwsf0NXjSFVByyCYdw6igACPMpcujVcRr7FEkox7RjUMSBIIgxEvbJGLJHHNKUqigZxQgHMBkSY1AkhjgfXVVFLlXBqtsASuaOqooJ3sPBi6awGHGIpo6CwfDs4zCa1uGlIJ428rY759Q1kiCIMWJOyKSIQ1mQ1MDwO2WqH/yKJJZjjiBVYWTO1WqF/QXZltNZZK4JUQ9egxGH5pAqCgftGOayw5CZuwFKavb5lVyawDHunZIwItMqvBKCIGqNaEAlj0O5kIsQDtl+C6osjdocqWVSFWLnyAr0cZCY0ABK4SkozBZxqFGPg5Cq4DE85/+qsI8RaIRWPxdDp91S6eUBABa3RfCscaqwTgDQ1TokFq+typoIgqgdogFF8DhIJBxKh09VBA+AJ5lwvk9mjnLMEQgH3TxZi+WY5R+rzRhDym6O5FpNVlXYaQr70IX6vH9XPTQFXVe9iZ4rXwAPtVR4dSZfPHMOprS04umZG4TtQ2d9E0akvSprIgiidogGFDHiUGWPQ02dabKjtV2Dn2xkIw4+ZQwRB8Mj4lCRckwgze0RB63io70rTVNIhQEJR9CIGTjuepwHGyvSfKsQq+c2YfXcJgArMLh5AMG3H0TyhEuQXHxZVddFEERtEA2oGLJ1j6x2qqKmzjR+RRJC+Z7kIg6SUI6JEQiHtF6lBlCMIQ3ryltxVVXU1J8TANAcNsXdYd6EGcwtHGBo7m1VJN5xPeId11d7GQRB1BBmqsI2ryLZV2Dv8lNTqQoz4jBMyiAbcZAlxG0ldCMxR6YzqQqVVbfltGmOrO2IQ1A1hdJh7j2DQkpU9x8QQRBEuYkGFMGALQ0dreJqakw4BBRJGALlRTYyoMoSEqMccmWlKirfctoeUVHhLMesPeGQJTuvwkl8yecrvBKCIIjKEg0oOMKtyb/S0JEqrqbGUhU+RRLMg55kShZVefTTMbMRB18VGkDZ+wXIk8AcCQDnL2yFsUucwfHu3Gsxp0FB/JSrqrMogiCIChENqDgMm3CIHzPTtI7f/C2H+vHgK/vQHUvhI+pmXOV/Dvzky5A84eKSrqemzjR+Rc7biCeLFXFgYqpiFBGHIJLW66qhfLuXDJc5EumabzkNADefOw8vqp8Cdm4EAMRP+jRaz/0mhqq8LoIgiEpQ74g4MG5Aih1z9Yn5wXM7seXwAPxI4XP+76GOxcEPvYLU9DNKWnVWU2ea4syRVh+HxCgbQGkZc2SApXLbuBLMt3vJcA658kETx2rXaMShNeLHpR/6CPp2/V/IfXuQOOnT1V4SQRBExagLKEjAjz4eQj0zxyNIQ0dcwmF3l/nYKWwXosw8pzEjBd/+F5BcdGnJ1lNTHge/ImEXH6ZuPhNxUCQmlGOOqKoi0zoyALtwKKJr5RgxG0BZVRUq9Ip3r6wmqXkfQrzjepo4SRDEpCIaMM9bos/hsLBPIq0jlk6v2Y0AABpWSURBVDbPB8ulHcJjvgN/LOl6ako4BBQJj+tnFt6JmblyRZIcsyqKH3KVLce0pypQgYgDY3BEHMRyzFpsOU0QBDHZifrN3317dZnTINkbt6YDr3AIB3X/iwB3t+kfLTUlHPyKhFeMk4raV5WZowFU8ZMVLY9DhSMOjs6RKrSaH3JFEAQx2fEpEgKKhKPIH3HojmWFA3dFHOShw5B73ivZempMOMjgkPA7/bRh9zWFw+g8DtmqigCzmSMrEnFggjnSxzTHaG+KOBAEQdQi0YCCw7ZUheyIOPRkhMMMdgytzN3fRj3wh5KtpcaEg/lxbk9fjQO8BXqBj6fKjlSFkSq6C6GmVyviIKYqnBGHWq2qIAiCmOzUB9WCHoeeuHk+WsG2ez5f6fpLydZSk8KhG1Gck/wRrm36l7z7usyRKD5doRkGAO4wR1amqiLlEg6TxxxJEAQxWanzF24ClY04nCAd9Hy+3LOrZGupSeEAABoU9MsNefdVnbMqgKIrK9I6hwpdHGmtViZVkZqE5ZgEQRCTHXf3SEfEISMcZjNLULxmLM7dVnpJOHhiFw7DoUjM1Syq2CZQaYOLFRWoTMRBlrxSFbU9q4IgCIIA6gOqWFWR7ANs3rzuTFXFLGbNsXheX2btHz8GluwvyVpqSjgoktiWmOXZDzDNkRzOQVfFlWRquiGkKYDKeBxcDaAYpSoIgiAmA9GAguOoF7ZJ8WO5270xt3B43ViIIZuXTy5R1KGmhANjzLkh776qbH50sSSz+IiD0DWSSYA0zFTOEiAxCEO83OZIqqogCIKoReoCCnTI6LeN15Zs47W7YylEMYRGNpjbtpdPxW5udZck4VAEhSIO2ehEDFakoFjhoDlSFVwJFhQppcIZcVDhHHJFfRwIgiBqkfqA+dvfx8O5bSxhCYfeeBozbdEGrgTRrzRiN5+a20bCYYxkIw4JIVVRpHDQDaEUsxJdI4Fsy2nRHCm2nKaIA0EQRC2SbTvdB5twSPbmbnfH0oIxUo/OwrzmMHbZIw59u0uylpoWDsVEHEYzryKtcwSF5k/l9zcA7pbTzogDp4gDQRBETVLnEXGQMsIhntaR1AzB36BHZ2Necwi7DJtw6NlZkrXUtJtOKuhxyAoH+7yKYvs4cPgr3MMByLactv5kfqZBZRRxIAiCqHWyqYpeIeJgpiq6B+P4vvKPuFx5PveYXj8bM5UgXnZGHDgfc2q9poVDfTD/x/NMVRQdcTAcXSMrIxwkR8tpAIKAoaoKgiCI2iSXqrBFHAKv/AD6tv/AgeMhXK68LOyvR2dhuhrEQd6S2yalh8BS/eB+sTpjpNRcqmLd6bMAmH6AG8+am3c/K1VhRRz+453duOWJLTgykMz3NADuPg6VEw5iqgJwtL0m4UAQBFGTRDMRh35EctsUaKjvfgsXSS+79jfqZ2NGQwBdiCLJrTS2NNg55rXU3JnmmlWzsaQ9ihkNQUyN5vceeJVj7j/ajRf1LsxuDOKm98/L+1xNN9DA7ObIyngcJIc5EgBC9kZUNKuCIAiiJgn7ZMhMjDjkQ6+bgdT00zE9JQNgOMSbMCdjnJQHD0FvPnFMaylZxOGhhx7Cbbfdlrt/0003YcmSJejo6EBHRwfWrFnj+bzu7m6sW7cOHR0duOCCC/Diiy+OaR2yxLBqThNmNJhRAM68P2LW4xCzNccIsSQAjkP9hSMOmsERQOXNkRKYO+LAbL4MijgQBEHUJIwx1AVUwePgRFdC6L78WXR/eiOgBNEQVBFSZRzizbl9ShFxGLNw0HUd//iP/4i/+7u/E7Zv3boVjz32GDZv3ozNmzfj8ccf93z+HXfcgZkzZ+K1117D7bffjltuuQXHjx8f67Is8giHbKoiYUtVfFF5Cq/6b0Rb3GO6GOeAboqFtM4RQNp6qFKpCsmdqrBHHChVQRAEUbtEA0rBiEN6zvnQW07KtQhgjGF6QwCHYGtVPXhozOsYs3DYsGEDNm3ahLVr1+a2DQ0NobOzEwsWLCj43KGhIWzcuBE33ngjfD4fzj77bKxYsQLPPPPMWJeVIzX7vNxtPTQld9srVQEAU1kP/vb4N8QXScfQ+MsL0fxPy+Db/XtohiGWY1ZgwBVgmiMNSNC49WcT+klQVQVBEETNEg0oQh8HOwYkxJd9wbV9en0Ah2wzLuTx4HG49dZb0dbWhvvvvx8HD5rjPLdv345QKIR169Zh27ZtWLx4Me644w7Mnz9feO7evXtRV1eHpibrQ82dOxe7dxfXpEKWJTQ2hgrv9PF7wB+5GEgNgq99JLd/U8YAGed+11MaeS80qRuonwEAkDbeA6nrXQBA/e8+D7Q/I5yw/eE6qMOtowQ0pM320imoUDKRhoBNwIQiYQQbQ5AzomjYYzMJoWNTGDo++aFjkx86NoUp1fFprvOj67BbOLxrzEZ4zX2Yvni167EFU+twaLeVqvAljox5HWOOOLS1tbm2xeNxLF26FHfccQdeeOEFLF++HDfccAPS6bSwXywWQzAoXq0HAgHE48WVRRZF3VTo178Kff3bQHtHbnO+iEMW9s6j1p0DrwmPmeWYNh9EhSIOcia9koYVWRDMkZSqIAiCqFnqgyp6bVUVWf4j8gm0n3ym53NmNoaEiAPrHwcRBy9Wr16N1ast5bN+/Xo8/PDD2LlzJxYvtuaDB4NBJBJi06VEIoFQqDg1pOsGenqKm2hpYkUJ4kPmCdfucRDY/Av0nHwDwBjqNV2QF4mUBr+tqiKuKYiNaB2jI5FZs72yImyLOAzFdSR7Yjk1ObJjMzmgY1MYOj75oWOTHzo2hSnV8Qkwhn4Pj8Mlpy9Db6/3BXeTTxLMkeg/gJ7uIYAxtLbWjWodZenjsHHjRjz99NO5+7quQ9d1+P3iSXr27NkYGBhAb6/Vb3v37t2YOzd//4VSoUqZiAP3jjjI/XuFPuB20jqvSgOologfsuSurMitgyIOBEEQNUs0oGAA7vNNY9usvM+Z1RhCp004MC2R99xWLGURDul0GnfeeSd27tyJVCqFu+++GwsXLnQJgkgkgrPOOgv33HMPkskk/vCHP+CNN97ABz/4wXIsS0CR3dMxnUiJHvMG58L2tGE4GkBVphxTkRim1wdc3SNzkHAgCIKoWaJBFdzjtG2Ep3jsbTIt6ocUbETC3gRqYGzpirIIhwsuuABXX301rr76aqxcuRI7duzAvffeCwDo7OxER0cHOjvNhd955504duwYzjjjDHznO9/Bj370I7S2tpZjWQJWOaZ3xAEAWMJblWk6R5BVPuIAmA7ZFPIMs6IhVwRBEDVL1J8n2uyL5n0OYwxLptcLUQd5aGwlmSW7RF2/fr1w/9prr8W1117r2q+9vR2bN2/O3W9ubsY//MM/lGoZRZMzR3pUVWTJRRwcpA2OQBXGagPAzIYg0p0UWSAIgphsZNtOuxhmaNWS9igO723CPBwGAEhDh8e0jpqbVVEs1nTMAhGHZA8O9SdwoEc0nWi6IQiHikYcGgLwIe3azsGgtYytjShBEAQxfskrHIbhlGlRdMOKSvBY95jWMYmFQ7Ycs1DEoRff/f0OHBsSW1AbulYVjwNgRhwWSGJ+anDVbei75JcwIu0VWwdBEARRWeoDo0tHL54SQS+sCor+niNjWsfkFQ5SERGHRA9e2eNOVzAjXTWPQ3YGh534ihuRnu5u/EEQBEHUDnWZiMOL+im5bUlbd+R8BFQZUsjyOAz1knAYFdlmSolCHoc8JSuSnnQMuaqccGivDwgtp7cqiyr23gRBEET1yKYq/rd2FXYa0xCvPwGD7/9uUc+NNFqVF+nBrjGtY9IKB8YYFIkhViBVka+qQjaSojmyQp0jAcCvSPi6dg0AswfF9wI3V+y9CYIgiOqhyhKuXTULB6R2/P2iRzB45XMw6opLUTe3TM3dlhNj8zhManu+KjMkDDFV0c0jaGKDAKyqCgmGsE+Ax+Fjeu4+lyvncQCAX+nn4k3jBHTzKM4/kSIOBEEQk4XrzpiDq1fNyvn0iqV96nTgf8zbEb0fxweT46tz5ERBlSUYjkNwgNt6SGSEgw+asE89hoT7lZqOmeUHHz8Je6WZYOEWXHN6/o5hBEEQRO0xUtEAAPXNVsShifXjnUMDo37/SR1xyDaBsvOCsRRLJHM6J4ubwkFISwBoyEQkslSyqgIAzjmhBc9cfzqCqgRlFF8ggiAIYnLBA42522GWxLv7jwEY3XiHSX3WyQqH29LXopeH8a/a2fhP/bTc41lzpN/RN6GVWd4HLvmACqcqANNdS6KBIAiCKAYj2CTcP3Do4Khfa1JHHLLhnsf0D+Ax/VwADNNxLPe4nB6EAg1+5hAO6MvdNvzRYbt2EQRBEERVkf3QlDAUzUy1D/UdHfVLTepL1mz3SBPzdg9Es0gDhlypCiHiUKBHOEEQBEGMF4yAFXVQkt4jFYphkgsH98ePwY8Ul3P369lg4VRFoL58CyQIgiCIUhGyhEMTRm+OnNTCwcscCTChNWcjBuB3RBza7MLBTxEHgiAIYvzDg1b3yEZGwmFUOCMO2a5cPTyS23aX+jPIjAv7CR4HH0UcCIIgiPEPt6Uqmkg4jA5nxKEhaA4Q6YUlHBZKbudpK0UcCIIgiAmG3ePQhP5Rv86kFg6iORKoz0Qcem0RBy/CzDanwk8RB4IgCGL8Yy/JpFTFKHGmKuozEYeXjZOLfg2DIg4EQRDEBIAHyRw5ZpypimzE4Z/1D+JHoa8U9RqcPA4EQRDEBMAIWiMVprLRD7qa1MIhX8SBQ8Iz0llFvQalKgiCIIiJgF4/J3d7FqMGUKPC6XHImiMBIKYBMSk87GtQqoIgCIKYCOj1s8CZedpXmDHM3vmZ1MLBlaqwCYfeuIYhDC8cqKqCIAiCmBDIfhh1M8f8MpNcOIgfvyFgje4YSGo4pg0/LptSFQRBEMREQWuYN+bXmNTCweBiYyd7xAEA+hEa/jVIOBAEQRATBJ2Ew9hwCocGh3Do40WkKnx1w+5DEARBEOMBvXH+mF9jUgsHXdQNaIv44bMZJvt54YhDHH5A9pVjaQRBEARRcvR6ijiMCcMQlUPEL+OWcy011j+MObKYiARBEARBjBcoVTFGnKkKxhjWLG3HxhtXAxheGPQNE5EgCIIgiPGEEZkKrgTG9BqTWjjoecpYI34FDUF1WHNkMeZJgiAIghg3MAnJuReO6SWU4XepXZwRBzutER/6uwoLA0pVEARBEBONgfN+jOTCNRhtTeCkjjgUEg5tEf+wHodZU6eWekkEQRAEUV5kFak554366ZNaOOhGfuHQEvENG1Fob59V6iURBEEQxLhmUguHpnD+Usr5LeFhPQy8aUGpl0QQBEEQ45pJLRyuP2MO/Ip5CG44c47w2CWnTIUaaij4fK1h7I00CIIgCGIiManNkS1hH5689jR09iXwV9PEDpBBVca9nz4D+Jf8z9cbKeJAEARBTC4mtXAAgOawD815UhbRuvwRB64EwINN5VoWQRAEQYxLJnWqYlgYy/uQXjejggshCIIgiPEBCYdRYtRNr/YSCIIgCKLikHAYJXqEIg4EQRDE5IOEwzD0XvKv2CfPxq/1M4TtBqUqCIIgiEkICYdhSE8/HS+d+wS+kv4SjnDLLJmcN7Ze3wRBEAQxESHhUATnLWzBZ06dgYdabkPflDMwcPad0JsWVntZBEEQBFFxJn05ZjFIjOGm988DMA8p/HW1l0MQBEEQVaNkEYeHHnoIt912m2v7K6+8gsWLF+d93vbt23HiiSeio6Mj998zzzxTqmURBEEQBFFCxhxx0HUdP/vZz3DPPffg4osvFh4bHBzEN77xDfACUyi3bt2Kc845Bz/5yU/GuhSCIAiCIMrMmCMOGzZswKZNm7B27VrXY3fddRcuuOCCgs/funVrwYgEQRAEQRDjhzELh1tvvRUPPPAAWltbhe0vvPAC9u3bhyuvvLLg87dt24Y33ngD5557Lj7wgQ/ggQceGOuSCIIgCIIoE2NOVbS1tbm29fX14a677sKDDz4IVqBtMwA0NDTg5JNPxhVXXIHOzk5cd911aG1txZo1a4Z9b1mW0NhYePT1ZESWTT1Ix8YNHZvC0PHJDx2b/NCxKUytHZ+ylGN++9vfxpVXXolZs2YNu+/dd9+Nq6++GqFQCAsWLMCVV16J5557rhzLIgiCIAhijJSlHPPZZ5/F888/j/vuuw+GYQAATj31VPz0pz/FqaeemtsvkUjg3nvvxQ033IC6OnOsdSqVgt/vL+p9dN1AT0+s9B9ggpNVtXRs3NCxKQwdn/zQsckPHZvCjNfj09paN6rnlUU4vPPOO7nbBw4cwHnnnYc33njDtV8gEMBLL70EzjluueUW7N69Gz//+c/xne98pxzLIgiCIAhijFSlc2RHR0dOSNx///3Ytm0bVq5ciXXr1uH666/H+9///mosiyAIgiCIYWC8UJOFcY6m6eMu9DMeGK9hsfEAHZvC0PHJDx2b/NCxKcx4PT6jTVXQrAqCIAiCIIqGhANBEARBEEVDwoEgCIIgiKKZ0B4HgiAIgiAqC0UcCIIgCIIoGhIOBEEQBEEUDQkHgiAIgiCKhoQDQRAEQRBFQ8KBIAiCIIiiIeFAEARBEETRTEjh8Pbbb+OSSy7BsmXL8OlPfxr79u2r9pKqyve//32ccsop6OjoQEdHB1auXAkAeP7553HhhRdi2bJluP7669Hd3V3llVaOhx56CLfddlvufqFjMRmPk/P43HTTTViyZEnuO7RmzZrcY48//jjOOeccdHR04NZbb0UikajGksvOf/3Xf+GjH/0oli9fjjVr1mDTpk0A6LsD5D829L0xeeaZZ/ChD30IHR0duPTSS7F582YAhc9VE/o8xicYiUSCn3HGGfypp57iyWSS33///fwTn/hEtZdVVa666ir+7LPPCtuOHj3KV6xYwV999VWeSCT4N77xDX7jjTdWaYWVQ9M0/sADD/DFixfzDRs2cM4LH4vJdpy8jg/nnH/wgx/kW7Zsce3/7rvv8pUrV/KtW7fygYEBvm7dOv7d7363kkuuCPv27ePLly/nr732Gtd1nT/55JP8tNNO452dnZP+u5Pv2AwMDEz67w3nnO/fv58vW7aMv/XWW5xzzv/t3/6Nn3322QXPVRP9PDbhIg6vvvoqGhoacNFFF8Hn8+GLX/wi9u/fj/fee6/aS6saW7duxeLFi4Vtv//977FixQqsXLkSfr8ft9xyC5577jkMDg5WaZWVYcOGDdi0aRPWrl2b21boWEy24+R1fIaGhtDZ2YkFCxa49v/tb3+Lj3zkI1i0aBEikQhuuukmPPnkk5VcckU4dOgQLrvsMpx22mmQJAkf//jHAQBPPPHEpP/u5Ds277777qT/3gDAjBkz8NJLL2Hp0qVIpVLo7e1FQ0NDwXPVRD+PTTjhsHv3bsybNy93X5ZlzJw5E7t27ariqqrH0aNH0dvbi7vuugurVq3CZZddhrfeegu7du0SjlNjYyMikQj27t1bxdWWn1tvvRUPPPAAWltbc9sKHYvJdpy8js/27dsRCoWwbt06rFq1CldddRV27twJwH3s5s6di+7ubvT29lZ87eXktNNOw4YNG3L333rrLcTjcezbt2/Sf3fyHRsAk/57kyUcDmPbtm1YunQp7rnnHtx6660Fz1UT/Tw24YRDLBZDIBAQtgWDwdwXebLR09OD0047DV/4whfw4osvYu3atbjuuuswNDQ0KY9TW1uba1s8Hs97LAo9VovkOz5Lly7FHXfcgRdeeAHLly/HDTfcgHQ67To+wWAQAGo6X713716sX78eX/7ylyFJEn13bNiPjaZp9L2xMW/ePLzzzjv41re+hfXr1xf8DZ7o57EJJxyCwaDryxePxxEOh6u0ouqyaNEiPPzww+jo6IDP58PatWvR1taGP/3pT3ScMhT6ztD3CVi9ejUefPBBLFiwAH6/H+vXr8fx48exc+dOBAIBJJPJ3L72K81a5J133sEVV1yByy67DNdccw19d2w4jw19b0RUVYWqqrj00ksxffr0mv7uTDjhMG/ePOzZsyd3X9d17Nu3D3Pnzq3eoqrIpk2b8Itf/ELYlkql8LnPfU44Tt3d3RgcHMSsWbMqvMLqM3fu3LzHotBjk4WNGzfi6aefzt3XdR26rsPv92PevHnYvXt37rHdu3ejpaUF0Wi0GkstK3/4wx/w+c9/HjfffDPWr18PgL47WbyODX1vTF5++WV8/vOfF7alUqmC56qJfh6bcMJh5cqV6OrqwhNPPIFUKoWf/OQnmDVrFubPn1/tpVUFn8+HH/zgB3jjjTegaRr++Z//GalUChdeeCFef/11/PGPf0QymcQ999yDc889d8Io2lJy/vnn5z0WhR6bLKTTadx5553YuXMnUqkU7r77bixcuBBz587FRz7yEfz2t7/Fli1bMDg4iPvuuw8XXXRRtZdccvbs2YObbroJd911l2Acpe9O/mND3xuTxYsXY8uWLXj66aehaRoeeeQR6LqOVatW5T1XTfjzWLXLOkbDn//8Z75mzRq+bNky/qlPfYrv3bu32kuqKr/+9a/5+eefz5cuXcovv/xyvnXrVs455y+++CL/8Ic/zDs6Ovi6det4V1dXlVdaOe677z6h3LDQsZiMx8l5fB588EF+9tln82XLlvFrrrmGd3Z25h779a9/zc877zy+fPly/tWvfpXH4/FqLLmsfO973+OLFi3iy5YtE/57/fXXJ/13p9Cxmezfmyyvv/46v/jii/mKFSv4Zz7zGb5z507OeeFz1UQ+jzHOOa+2eCEIgiAIYmIw4VIVBEEQBEFUDxIOBEEQBEEUDQkHgiAIgiCKhoQDQRAEQRBFQ8KBIAiCIIiiIeFAEARBEETRkHAgCIIgCKJoSDgQBEEQBFE0JBwIgiAIgigaEg4EQRAEQRQNCQeCIAiCIIqGhANBEARBEEVDwoEgCIIgiKIh4UAQBEEQRNGQcCAIgiAIomhIOBAEQRAEUTQkHAiCIAiCKJr/H6R3i5vgLs+SAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 600x450 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data[['收盘价(元)','开盘价(元)']].plot()"]},{"cell_type":"markdown","metadata":{},"source":["### 59.绘制涨跌幅的直方图"]},{"cell_type":"code","execution_count":79,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12bcf2310>"]},"execution_count":79,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 600x450 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.hist(df['涨跌幅(%)'])\n","# 等价于\n","df['涨跌幅(%)'].hist()"]},{"cell_type":"markdown","metadata":{},"source":["### 60.让直方图更细致"]},{"cell_type":"code","execution_count":80,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12be9bbd0>"]},"execution_count":80,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 600x450 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data['涨跌幅(%)'].hist(bins = 30)"]},{"cell_type":"markdown","metadata":{},"source":["### 61.以data的列名创建一个dataframe"]},{"cell_type":"code","execution_count":81,"metadata":{},"outputs":[],"source":["temp = pd.DataFrame(columns = data.columns.to_list())"]},{"cell_type":"markdown","metadata":{},"source":["### 62.打印所有换手率不是数字的行"]},{"cell_type":"code","execution_count":82,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>代码</th>\n","      <th>简称</th>\n","      <th>日期</th>\n","      <th>前收盘价(元)</th>\n","      <th>开盘价(元)</th>\n","      <th>最高价(元)</th>\n","      <th>最低价(元)</th>\n","      <th>收盘价(元)</th>\n","      <th>成交量(股)</th>\n","      <th>成交金额(元)</th>\n","      <th>涨跌(元)</th>\n","      <th>涨跌幅(%)</th>\n","      <th>均价(元)</th>\n","      <th>换手率(%)</th>\n","      <th>A股流通市值(元)</th>\n","      <th>总市值(元)</th>\n","      <th>A股流通股本(股)</th>\n","      <th>市盈率</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>26</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-16</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-17</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-18</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-19</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-22</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-23</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-24</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-25</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-26</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-29</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-01</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-02</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-03</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-04</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-07</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-08</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-09</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-10</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n","26  600000.SH  浦发银行 2016-02-16  16.2946  16.2946  16.2946  16.2946  16.2946   \n","27  600000.SH  浦发银行 2016-02-17  16.2946  16.2946  16.2946  16.2946  16.2946   \n","28  600000.SH  浦发银行 2016-02-18  16.2946  16.2946  16.2946  16.2946  16.2946   \n","29  600000.SH  浦发银行 2016-02-19  16.2946  16.2946  16.2946  16.2946  16.2946   \n","30  600000.SH  浦发银行 2016-02-22  16.2946  16.2946  16.2946  16.2946  16.2946   \n","31  600000.SH  浦发银行 2016-02-23  16.2946  16.2946  16.2946  16.2946  16.2946   \n","32  600000.SH  浦发银行 2016-02-24  16.2946  16.2946  16.2946  16.2946  16.2946   \n","33  600000.SH  浦发银行 2016-02-25  16.2946  16.2946  16.2946  16.2946  16.2946   \n","34  600000.SH  浦发银行 2016-02-26  16.2946  16.2946  16.2946  16.2946  16.2946   \n","35  600000.SH  浦发银行 2016-02-29  16.2946  16.2946  16.2946  16.2946  16.2946   \n","36  600000.SH  浦发银行 2016-03-01  16.2946  16.2946  16.2946  16.2946  16.2946   \n","37  600000.SH  浦发银行 2016-03-02  16.2946  16.2946  16.2946  16.2946  16.2946   \n","38  600000.SH  浦发银行 2016-03-03  16.2946  16.2946  16.2946  16.2946  16.2946   \n","39  600000.SH  浦发银行 2016-03-04  16.2946  16.2946  16.2946  16.2946  16.2946   \n","40  600000.SH  浦发银行 2016-03-07  16.2946  16.2946  16.2946  16.2946  16.2946   \n","41  600000.SH  浦发银行 2016-03-08  16.2946  16.2946  16.2946  16.2946  16.2946   \n","42  600000.SH  浦发银行 2016-03-09  16.2946  16.2946  16.2946  16.2946  16.2946   \n","43  600000.SH  浦发银行 2016-03-10  16.2946  16.2946  16.2946  16.2946  16.2946   \n","\n","   成交量(股) 成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)     A股流通市值(元)        总市值(元)  \\\n","26     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","27     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","28     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","29     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","30     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","31     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","32     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","33     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","34     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","35     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","36     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","37     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","38     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","39     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","40     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","41     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","42     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","43     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","\n","       A股流通股本(股)    市盈率  \n","26  1.865347e+10  6.801  \n","27  1.865347e+10  6.801  \n","28  1.865347e+10  6.801  \n","29  1.865347e+10  6.801  \n","30  1.865347e+10  6.801  \n","31  1.865347e+10  6.801  \n","32  1.865347e+10  6.801  \n","33  1.865347e+10  6.801  \n","34  1.865347e+10  6.801  \n","35  1.865347e+10  6.801  \n","36  1.865347e+10  6.801  \n","37  1.865347e+10  6.801  \n","38  1.865347e+10  6.801  \n","39  1.865347e+10  6.801  \n","40  1.865347e+10  6.801  \n","41  1.865347e+10  6.801  \n","42  1.865347e+10  6.801  \n","43  1.865347e+10  6.801  "]},"execution_count":82,"metadata":{},"output_type":"execute_result"}],"source":["for i in range(len(data)):\n","    if type(data.iloc[i,13]) != float:\n","        temp = temp.append(data.loc[i])\n","\n","temp"]},{"cell_type":"markdown","metadata":{},"source":["### 63.打印所有换手率为--的行"]},{"cell_type":"code","execution_count":84,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>代码</th>\n","      <th>简称</th>\n","      <th>日期</th>\n","      <th>前收盘价(元)</th>\n","      <th>开盘价(元)</th>\n","      <th>最高价(元)</th>\n","      <th>最低价(元)</th>\n","      <th>收盘价(元)</th>\n","      <th>成交量(股)</th>\n","      <th>成交金额(元)</th>\n","      <th>涨跌(元)</th>\n","      <th>涨跌幅(%)</th>\n","      <th>均价(元)</th>\n","      <th>换手率(%)</th>\n","      <th>A股流通市值(元)</th>\n","      <th>总市值(元)</th>\n","      <th>A股流通股本(股)</th>\n","      <th>市盈率</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>26</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-16</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-17</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-18</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-19</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-22</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-23</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-24</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-25</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-26</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-02-29</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-01</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-02</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-03</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-04</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-07</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-08</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-09</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>2016-03-10</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>16.2946</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>--</td>\n","      <td>--</td>\n","      <td>3.441565e+11</td>\n","      <td>3.441565e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.801</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n","26  600000.SH  浦发银行 2016-02-16  16.2946  16.2946  16.2946  16.2946  16.2946   \n","27  600000.SH  浦发银行 2016-02-17  16.2946  16.2946  16.2946  16.2946  16.2946   \n","28  600000.SH  浦发银行 2016-02-18  16.2946  16.2946  16.2946  16.2946  16.2946   \n","29  600000.SH  浦发银行 2016-02-19  16.2946  16.2946  16.2946  16.2946  16.2946   \n","30  600000.SH  浦发银行 2016-02-22  16.2946  16.2946  16.2946  16.2946  16.2946   \n","31  600000.SH  浦发银行 2016-02-23  16.2946  16.2946  16.2946  16.2946  16.2946   \n","32  600000.SH  浦发银行 2016-02-24  16.2946  16.2946  16.2946  16.2946  16.2946   \n","33  600000.SH  浦发银行 2016-02-25  16.2946  16.2946  16.2946  16.2946  16.2946   \n","34  600000.SH  浦发银行 2016-02-26  16.2946  16.2946  16.2946  16.2946  16.2946   \n","35  600000.SH  浦发银行 2016-02-29  16.2946  16.2946  16.2946  16.2946  16.2946   \n","36  600000.SH  浦发银行 2016-03-01  16.2946  16.2946  16.2946  16.2946  16.2946   \n","37  600000.SH  浦发银行 2016-03-02  16.2946  16.2946  16.2946  16.2946  16.2946   \n","38  600000.SH  浦发银行 2016-03-03  16.2946  16.2946  16.2946  16.2946  16.2946   \n","39  600000.SH  浦发银行 2016-03-04  16.2946  16.2946  16.2946  16.2946  16.2946   \n","40  600000.SH  浦发银行 2016-03-07  16.2946  16.2946  16.2946  16.2946  16.2946   \n","41  600000.SH  浦发银行 2016-03-08  16.2946  16.2946  16.2946  16.2946  16.2946   \n","42  600000.SH  浦发银行 2016-03-09  16.2946  16.2946  16.2946  16.2946  16.2946   \n","43  600000.SH  浦发银行 2016-03-10  16.2946  16.2946  16.2946  16.2946  16.2946   \n","\n","   成交量(股) 成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)     A股流通市值(元)        总市值(元)  \\\n","26     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","27     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","28     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","29     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","30     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","31     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","32     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","33     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","34     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","35     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","36     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","37     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","38     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","39     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","40     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","41     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","42     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","43     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n","\n","       A股流通股本(股)    市盈率  \n","26  1.865347e+10  6.801  \n","27  1.865347e+10  6.801  \n","28  1.865347e+10  6.801  \n","29  1.865347e+10  6.801  \n","30  1.865347e+10  6.801  \n","31  1.865347e+10  6.801  \n","32  1.865347e+10  6.801  \n","33  1.865347e+10  6.801  \n","34  1.865347e+10  6.801  \n","35  1.865347e+10  6.801  \n","36  1.865347e+10  6.801  \n","37  1.865347e+10  6.801  \n","38  1.865347e+10  6.801  \n","39  1.865347e+10  6.801  \n","40  1.865347e+10  6.801  \n","41  1.865347e+10  6.801  \n","42  1.865347e+10  6.801  \n","43  1.865347e+10  6.801  "]},"execution_count":84,"metadata":{},"output_type":"execute_result"}],"source":["data[data['换手率(%)'].isin(['--'])]"]},{"cell_type":"markdown","metadata":{},"source":["### 64.重置data的行号"]},{"cell_type":"code","execution_count":85,"metadata":{},"outputs":[],"source":["data = data.reset_index()"]},{"cell_type":"markdown","metadata":{},"source":["### 65.删除所有换手率为非数字的行"]},{"cell_type":"code","execution_count":86,"metadata":{},"outputs":[],"source":["k =[]\n","for i in range(len(data)):\n","    if type(data.iloc[i,13]) != float:\n","        k.append(i)\n","data.drop(labels=k,inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["### 66.绘制换手率的密度曲线"]},{"cell_type":"code","execution_count":87,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12c01d8d0>"]},"execution_count":87,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAhIAAAGFCAYAAACothrZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deZyVdd3/8fdZZubMvsCwg2wGoiiyiqilFnarmMuNPyurW7vLJZcemlup4N2iZd2ZRd5RmUvpnUuhUoamhha3GSRqCigMMCDMMPt6zpzt+v0xzDnXgWEYzpyZazmv5+PBg3OdOefMhy+Hw3u+q8cwDEMAAABp8FpdAAAAcC6CBAAASBtBAgAApI0gAQAA0kaQAAAAaSNIAACAtBEkAABA2ggSAAAgbQQJAACQNoIEAABIG0ECAACkjSABAADSRpAAAABpI0gAAIC0+a0uIFPq6tqsLkHl5QWSpKamTosrcRbaLT20W3pot/TQbulxSrtVVhan/Vx6JAAAQNoIEgAAIG0ECQAAkDaCBAAASBtBAgAApI0gAQAA0kaQAAAAaSNIAACAtBEkAABA2ggSAAAgbQQJAACQNoIEAABIG0ECWS0UiamuvUuGYVhdCgA4kmtO/wSO1J+31OnbL76v9q6YZowq1g8+NUPDi/KsLgsAHIUeCWSl7Q2duvP5zWrvikmS3qtp09f/sJmeCQA4QgQJZKVfr9+lSCw1NLy5u0Vv7Gy2qCIAcCaCBLJOczCiP23a1+vXfv/O3iGuBgCcjSCBrLNue6PC+3sjKgpy9N0lxyS+tnZrg1pDEatKAwDHIUgg66zb3pi4fdqUYTpt6nBVFORIkqJxg+ENADgCBAlklbhh6O+moLBwYrn8Xo8WHFWeuO/1HU1WlAYAjkSQQFbZ2RhUc7B76MLrkeZN6A4QJ000BYmdBAkA6C+CBLLKezVtidsTKwpUHOjeSmW+qUeitq1LtW1dQ14bADiRLYPEG2+8oSVLlmj27Nm66KKL9Pbbb1tdElxiU20ySMwYVZy4PbwwV6NLkptRvbu3dUjrAgCnsl2QiMViuvbaa3Xrrbdqw4YNuvDCC/XVr37V6rLgEuYeCXOQkKTjRpckbr+zt00AgMOzXZBoaWlRc3OzotGoDMOQz+dTXh7bFmPg4oah9+s6EtfHjCxK+fpxo5PBgh4JAOgf2521UVFRoaVLl+rLX/5yIkQ8/PDDVpcFF9jbGlJXNJ64njK8MOXrx4xMBokP6jtkGIY8Hs+Q1QcATmS7IBGLxVRUVKRf/vKXmj9/vh5//HFdf/31+uMf/6j8/PxDPq+8vGAIq+ydz9fdwWOHWpxkqNpt4772xO1xZfkaMyJ1aGNOICdxu70rpi6fT6NLA4Na00DwfksP7ZYe2i092dButhvaWLNmjaqqqnTKKacoNzdXX/jCF5Sbm6t169ZZXRocbuu+5LDGlBGFB329ND9Ho0qSweH9WuZJAMDh2K5Hora2VpFI6hbFOTk58vv7LrWpqXMwy+qXnsRph1qcZKja7d3dyY2oxhUHev1+kyryVdMakiS9taNRx1ceHDjsgvdbemi39NBu6XFKu1VWFh/+QYdgux6JhQsXasOGDXrxxRcVj8f19NNPq7m5WXPmzLG6NDjczsbkP+RJw3ofJjPPm9hW39HrYwAASbYLEtOnT9e9996r++67T/PmzdOTTz6plStXqqio6PBPBvrwYUsocXt8ee9BYqopSGytt/dPEABgB7Yb2pCks846S2eddZbVZcBFOsMxNXYmh8zGlh6qRyI5IWp7Q4eicUN+Lys3AOBQbNcjAQyGPa3J3ogcn0eVRbm9Pm5iRYF6YkM4ZiTmSwAAekeQQFb4sDkZCEaXBOQ9xP4QgRyfRhYnN0CrbgoOem0A4GQECWQFc4/EmMPsDWGeP7GLIAEAfSJIICt82JwMBGMPEyQmmINEM0ECAPpCkEBW2GNasXG4IDGuLBkkGNoAgL4RJJAVzEs/Dzu0YQoSu+mRAIA+ESTgeoZhHFGPhHloY09LSNFYvI9HA0B2I0jA9VqCUYVMp36OLuk7SIwtDahn64iYIe1p7RrM8gDA0QgScL3a9mQQyPN7VRLoex+2XL9Xo0xLQFm5AQCHRpCA69WZgsSIolx5DrGHhJl5wqV5fgUAIBVBAq63rz2cuD3C1NPQl9GmeRR72d0SAA6JIAHXq2tL9khUFvUzSJQkH0eQAIBDI0jA9fYdMLTRH+YJmXuZbAkAh0SQgOulDG30s0dijDlIMEcCAA6JIAHXM0+2rOznHIlRpqGNpmBEwUgs43UBgBsQJOB6dSk9Ev0b2qgsypPPm1zdwTwJAOgdQQKuForE1BqKJq77O9nS5/Wk7CXBPAkA6B1BAq5mnh/h9UjDCvvXIyEdsHKDeRIA0CuCBFzNPD9iWGGu/N7Db0bVI3XlBkECAHpDkICr1Zt6JIYfQW+ElLop1Z4WhjYAoDcECbhaQ2cySBzJsIaUOrRR00aPBAD0hiABV2vsjCRuVxTkHNFzzUMbe5gjAQC9IkjA1Ro7kj0SFQVH1iMxxjS00dgZUYi9JADgIAQJuFpKj8QRDm1UFubKPDXTvB8FAKAbQQKu1mieI3GEQxt+n1fDTRtYmc/sAAB0I0jA1RoGMLQhpZ7NUdtGkACAAxEk4FqGYagpaB7aOLIeCUkaYdrdch9BAgAOQpCAa7V1RRWJGYnr9Hokks+hRwIADkaQgGs1diR7I3xej0oC/iN+jZHmHgkmWwLAQQgScC3zZlQVBTnyevq/PXaPkQxtAECfCBJwrdTNqI58WENKnWzJqg0AOBhBAq6VuhnVkU+0lFInWzZ2RhSOxgdcFwC4CUECrmXeQ+JIN6PqUVmU+jx6JQAgFUECrtVgGto40s2oeuT4vCm9GQQJAEhFkIBrDeScDbPUCZes3AAAM4IEXCv1nI30eiQkVm4AQF8IEnCtps7M9EiwcgMADo0gAVcyDCNljkS6qzak1JUb7G4JAKkIEnClUDSuLtNSzfKB9EgUs002ABwKQQKu1Gw6rEuSytLYHrtH6tAGky0BwIwgAVcyB4niPL/8vvTf6ubJlo0dYUVibEoFAD0IEnAlc5Aoy0+/N0KSKk09EoakOnolACCBIAFXSg0S6U+0lKQ8vzflNepYuQEACQQJuFJzMJq4XTrAICGlbpXNPAkASCJIwJUy2SMhpU64pEcCAJIIEnCllgwHiZQeCbbJBoAEggRcydwjUTqApZ89zJtSsbslACQRJOBKmR/aSPZIMLQBAEkECbhSpoNEJZtSAUCvCBJwJfOqjUxPtqxv75JhGAN+TQBwA4IEXMcwjEHokUgObYRjhlpMQQUAshlBAq7TEY4pFk/2GGQiSJQE/MrzJ/+5MOESALoRJOA65t4Ij6TiDKza8Hg8Kb0SbJMNAN0IEnAd8x4SJQG/fF5PRl439RRQeiQAQCJIwIUyPdGyR+qmVAQJAJAIEnChTE+07JG6TTZDGwAgESTgQoMVJCrZ3RIADkKQgOsMXo8Eky0B4EAECbhOyjkbGZ0jwQmgAHAgggRcJ7VHYuBLP3uYeyRaQlGFIrGMvTYAOBVBAq7TEkqu2igNZK5HYnhhrswLSes7GN4AAIIEXKfNFCRKMrAZVQ+/z6uKQtMSUIY3AIAgAfdpDZk2pMrg0IaUOryxr40eCQAgSMB1WlJ6JDI3tCEx4RIADmTLIFFdXa0vfOELOvHEE3XWWWdp7dq1VpcEhwhFYuqKxhPXJXmZ7ZFI2d2SJaAAYL8gEY/H9ZWvfEUnnXSSNmzYoNtvv11f/epX1dnZaXVpcIC2rtTjvTM5R0I6cHdLeiQAILOfshnw5ptvKhQK6corr5TH49Gpp56qxx9/XD6fz+rS4ACtpmGNPL9XgZzMvm8qmSMBACls1yOxefNmTZkyRXfeeadOOukkXXDBBWpvb1deXt7hn4ys1zpIKzZ60CMBAKls1yPR0tKiV199VXfeeafuuOMOvfDCC/rKV76iNWvWqKys7JDPKy8vGMIqe+fzdecyO9TiJJlst1hNW+J2eWFuxv8upkaS8y/qO8IqLc2XN0PHlB8p3m/pod3SQ7ulJxvazXY9Ejk5ORozZowuueQS5ebm6txzz9WIESP0z3/+0+rS4AAtnabtsTO8YkOSRpYEErejcUONnQxvAMhutuuRmDRpktrb21Pui8fjMgyjz+c1NVk/GbMncdqhFifJZLvtbUy+RoHfOyh/FwU5PnXu3x77/d3N8o0szvj36A/eb+mh3dJDu6XHKe1WWZn+55jteiROPvlk+Xw+PfTQQ4rH43rmmWdUX1+vBQsWWF0aHKDNvBnVIMyRkJhwCQBmtgsSBQUFeuSRR/TSSy9p3rx5+sUvfqEVK1aoqKjI6tLgAIO5GVWPymImXAJAD9sNbUjSlClT9Oijj1pdBhzIvGqjNMPbY/cwb5NNkACQ7WzXIwEMhPmcjeIM72rZw7xNNrtbAsh2BAm4ymDvIyHRIwEAZgQJuIp5jsRgLP+UUjelokcCQLYjSMBV2sw9EoM0R4LJlgCQRJCAa8TiRsqhXUMxtNHeFVNnODYo3wcAnIAgAdc46OTPvMEZ2qgoyJXPtCv2PnolAGQxggRcwzzR0uuRCvMG58RYn9ejYYVMuAQAiSABFzlw6afXM3iHaVWmnALKhEsA2YsgAddI3YxqcIY1eqRuk02PBIDsRZCAawzFHhI9RhbTIwEAEkECLjIUu1r2SN3dkh4JANmLIAHXaBnCHomUoQ16JABkMYIEXKN1CHa17DGiiE2pAEAiSMBF2kxDG0PZI9HQEVY0bgzq9wMAuyJIwDVShjYGedXGCNNky7ghNXYwvAEgOxEk4BopqzYGebJlfo5PRaYNrxjeAJCtCBJwjdYhHNqQDly5QY8EgOxEkIBrDOU+EpI0kgmXAECQgDsYhpEyR2KwV21ILAEFAIkgAZcIRuKKmVZOlOQPwdCGacIl22QDyFYECbiCeX6ENPiTLSVpRBEngAIAQQKuYB7WKMjxye8b/Lc2ky0BgCABlxjqFRvSwT0ShsGmVACyD0ECrjDUKzak1B6JYCSujnBsSL4vANgJQQKu0DqEu1r2KC/Ikd/rSVxzCiiAbESQgCsM5a6WPbweT8oS0Lo25kkAyD4ECbiCFXMkpAMnXNIjASD7ECTgCikHdg3BZlQ9RqRsSkWQAJB9CBJwhdaUXS2t6ZGoYwkogCxEkIArtFk2tGHqkWB3SwBZiCABV2ixYPmnJI2gRwJAliNIwBVaLZojUVnMHAkA2Y0gAVewatWGuUeiqTOiaCw+ZN8bAOyAIAHHC0fjCkaS/4FbtfzTkFTfwfAGgOxCkIDjtXZFU66Hcmgjz+9NWSXC4V0Asg1BAo7XZpof4fd6lJ8ztG/rEcWmTalYuQEgyxAk4HgHzo/weDx9PDrzKtmUCkAWI0jA8aw4+dOMTakAZDOCBBzPHCSK84ZufkQP8zbZdfRIAMgyBAk4nnmyZWm+tT0STLYEkG0IEnC81mByjkTxEB0hbmaebEmPBIBsQ5CA47V1WTtHInVoIyzDMIa8BgCwCkECjmenyZZd0XhKPQDgdgQJOJ5V52z0KA34ledP/lNiCSiAbJKRILF8+XKtX78+Ey8FHDGreyQ8Ho9GmuZJ1LQSJABkj4x86g4fPlzLly9Xa2urPvnJT+qcc87RCSeckImXBg7LqgO7zEYV56m6KShJ2tsasqQGALBCRnokrrnmGq1evVoPPvigSkpKtGzZMn384x/XD37wA23atCkT3wI4JPNkSytWbUjS6NJA4vaeFnokAGSPjM6RmDp1qpYsWaIlS5YoPz9fTzzxhL72ta/pvPPOY+gDg8IwDLWYhjZKLZgjIUljSpJBoqaNHgkA2SMjP7598MEHWrNmjdasWaOamhqdeeaZ+trXvqZFixbJ7/fr6aef1nXXXad169Zl4tsBCcFIXLF4crllsVVDGyXJORJ7WggSALJHRj51L774Yp1++um6/vrrddpppyk3Nzfl6wsXLtTixYsz8a2AFOb5EZJ1cyTMPRJ7mWwJIItk5FN32bJlOv/88w+6/8knn9TSpUs1ZswYLV++PBPfCkhhXrGRn+NVjs+aFc3mORLNwYiCkZjyc3yW1AIAQyntINHU1KStW7dK6l7+OW7cuJQd/drb2/Wd73xHS5cuHXiVwCFYvYdEj+GFufJ5PYlhlr2tIU0eVmhZPQAwVNIOEoFAQHfffbeam5vV1dWlm2++OeXrubm5+vKXvzzgAoG+tFq8PXYPn9ejUcV5+nD//Ii9LV0ECQBZIe1P3vz8fP3ud7+TJN1888363ve+l7GigP4yH9hlZZCQuoc3eoLEHvaSAJAlBvTJu2HDBs2ZM0dLly7VP/7xj14fM2/evIF8C6BPdthDosfolN0tCRIAssOAPnmXLVum1atX65Zbbun16x6PRy+99NJAvgXQJzvsIdGDTakAZKMBBYnVq1dLkl5++eWMFAMcqTZTkLBqD4keqUtA6ZEAkB0yslYuEono2WeflSTt3r1b1113ne688041NTVl4uWBQ7LDORs9zJtSESQAZIuMBIlly5bp4YcfliTddttt8vv9ikQi+sY3vpGJlwcOyeqTP83GmIY2GjsjCkViFlYDAEMjI5+869at0+rVq7Vv3z7985//1Guvvabi4mItXLgwEy8PHJJd9pGQpMqiPPk8Umz/dio1rV2aOKzA0poAYLBlpEciFAopNzdXf/nLX3TMMceooqJCbW1tysmx9oMd7peyj4TFqzb8Xo9GmFZusAQUQDbIyCfv4sWLddlll2n79u264YYbtG3bNt14440655xzMvHywCGlzJHItzZISNLY0kDirI0PObwLQBbIyCfv8uXL9cILL6i4uFiLFi3Srl279NnPflYXXXRRJl4e6FUsbqi9KzkPwep9JCRpbFm+1u9qkSTtbg5aXA0ADL6MDG14vV6dccYZmjJlivbs2SOfz6dFixappqYmEy8P9KrdNKwhWb+PhCSNL8tP3N7VRJAA4H4Z+RHuqaee0t13363Ozs6Ug7s8Ho82bdqUiW8BHMQ80dIjqTDP+tM2x5clV27sZmgDQBbISJD46U9/qjvuuENLliyRz5e5D/OtW7fqggsu0PPPP69x48Zl7HXhDgce2OX1eCyspts4U4/Eh81BxQ3DFnUBwGDJyNBGR0dHxkNENBrVbbfdpnA4nLHXhLuYJ1pavatlj7GmHolwzNC+NrbKBuBuGQkSn/70p3Xfffepubk5Ey8nSfrZz36mOXPmZOz14D5tNtpDokdhrl8VBcladjczvAHA3TLyY9zvf/971dbW6he/+EXiPsMw0p4jsXnzZj3//PN66qmn9Ktf/apfzykvt37jH5+vO5fZoRYnSbfdIt5kDq4oyrVNu08aXqjG6u5Q3RiJDVpdvN/SQ7ulh3ZLTza0W0aCxGOPPZaJl5EkhcNh3XbbbbrrrrsUCAQO/wRkrdZgcmijLN8ePRKSdNSwAm3YHyR2NnRaXA0ADK6MBImxY8eqs7NTa9euVU1NjS655BJt3bpVM2fOPOLXWrFihebPn3/EwxpNTdZ/YPckTjvU4iTptluN6fF5Xo9t2r3SFGq21rQNWl2839JDu6WHdkuPU9qtsrI47edmZI7Eu+++q8WLF+vhhx/W/fffr/r6en3+85/XqlWrjvi11qxZo6eeekpz587V3LlzJUnnnXeennvuuUyUChcxz5EotclkSyl1Lwk2pQLgdhn59P2v//ov3X777frkJz+pefPmafz48frFL36h2267Teeff/4Rvdaf/vSnlOtp06bp2WefZfknDmLeR6LYJpMtJWlcuTlIhBLzhQDAjTLSI1FVVaXFixdLUuIDc86cOWpsbMzEywO9OnAfCbswb0rVGYmpsTPSx6MBwNkyEiSmTp2q559/PuW+v/zlL5o6deqAX3vLli30RqBXKQd22eCcjR4lgZyUoRaGNwC4WUY+fW+//XZ96Utf0m9+8xt1dnbqiiuu0L/+9S898MADmXh5oFcp+0jY4ORPs7Fl+WqpaZMk7WoO6oSxpRZXBACDIyOfvscee6xeeOEFrV27VmeccYaGDx+u7373uyorK8vEywO9ajEHiTz7zJGQuoc33tsfJKo5vAuAi2UkSLS2tuqtt96Sx+PR7NmzdfTRR6u4OP2lJMDhdEXj6orGE9d2miMhSRMrkpvPbGcvCQAuNqBP33g8ru9973v69a9/Lb/fr9LSUrW2tioajeqyyy7TDTfckKk6gRRtodQJjHYLEpOGJYPEzkZ6JAC414A+fVesWKFXX31VDz/8cGIDKcMwtGHDBi1btkxlZWW6/PLLM1IoYGZesZHr8yjPn5F5wxlzlKlHoro5qGgsLr/PXjUCQCYM6JPtmWee0Q9/+MOUXSg9Ho/mzp2re+65R08//fSACwR60xpM3UPCbvs0TCjLl3d/SbG4od0tHN4FwJ0GFCQaGho0bdq0Xr82c+ZM1dbWDuTlgUOy6x4SPXL9Xo0tTe4nsYN5EgBcakBB4nA/Bcbj8T6/DqTLrntImJknXO5oJEgAcKcBfQIbhqG9e/fKMIxDfh0YDObtse3YIyF1B4nXqrp3dyVIAHCrAX0CB4NBnXHGGYcMDHYbt4Z7tDghSAwz90iwcgOAOw3oE3jz5s2ZqgM4Ii3B5NBGab69NqPqceDQBod3AXAj1qPBkVqC5iPE7RokkqeAdoRjqmsPW1gNAAwOggQcqSVk7pGw59BGSSBHlUW5ieut9R0WVgMAg4MgAUdKGdqwaY+EJE0ZXpi4vY0gAcCFCBJwJPNkS7v2SEjS0aYg8UEdQQKA+xAk4EhO6ZGYWpkMEgxtAHAjggQcpysaV8h08qddV21I0tGmILG9oVORGJu0AXAXggQcx9wbIUmlNt1HQupeAurbf+hGNG5wEigA1yFIwHHMKzby/F4FcnwWVtO3HJ83ZRkowxsA3IYgAcdJ3UPCvr0RPaYy4RKAixEk4Dipe0jYd35Ej6MrixK3P6hrt7ASAMg8ggQcxwnbY5uZV268X9fBYXYAXIUgAccx7yFR5oChjWNGJnskGjrCbJUNwFUIEnCcZof1SFQU5GpUcV7ielNtm4XVAEBmESTgOCm7WjqgR0KSjhlVnLj9Xi3zJAC4B0ECjuO0ORJS6vDGphp6JAC4B0ECjuOEI8QPNGNkskdic207Ey4BuAZBAo7jhCPEDzTd1CPRFIyotq3LwmoAIHMIEnAcpxzYZVaan6OxpYHENfMkALgFQQKOEjcMtXWZjxB3RpCQpGNMwxvv7m21sBIAyByCBBylLRRV3DS9wCmrNiTpuNHJIPHWhwQJAO5AkICjmJd+ej1SsYOCxAljSxK3N9W2KRzlSHEAzkeQgKOY50cU5/nl9XgsrObITBtRpDx/9z+5cMxgYyoArkCQgKM47cAusxyfV8eaNqZ6ew/DGwCcjyABR3HiHhJm5uGNjcyTAOACBAk4ihP3kDA7YUxp4vbbe1rZmAqA4xEk4CjmORJlDhvakKSZY5JDG83BiHY2Bi2sBgAGjiABR0k9sMt5QaIkkKOpwwsT1//Y1WxhNQAwcAQJOEpTp7lHwnlDG5I0/6iyxO03djZZWAkADBxBAo7S1BlO3K4oyLWwkvTNm5AMEht2tSgWZ54EAOciSMBRmsxzJAqcN7QhSSeOK5XP273/RVtXVFv2ce4GAOciSMBRzEMb5Q6cbClJhbl+HWfaT4LhDQBORpCAY8TihlpNky3LHdojIaUOb7xRzYRLAM5FkIBjtIQiMs8mcOLyzx7zjypP3N74YYs6wzELqwGA9BEk4BjmYY1cn0eFuT4LqxmYmaOLVZzXveokEjP0d4Y3ADgUQQKOkbr0M0ceBx3YdSC/z6uTJyV7Jf5a1WBhNQCQPoIEHMO8YqPcoUs/zU6ZPCxx+69VjYqzXTYAByJIwDHcsGLDbOHEcvn2d6o0dka0qYZjxQE4D0ECjtEcTG5G5dQ9JMxK83N0/NjkIV6vbmN4A4DzECTgGOYeiQoXBAlJOnVyReL2n9+v5zRQAI5DkIBjNDv85M/efHxaZeJ2dVNQ79d1WFgNABw5ggQco9FlcyQkaXRJQDNHJ3e5fHFLnYXVAMCRI0jAMVJXbbgjSEipvRIvbqljeAOAoxAk4BjNne4b2pCkj3+kUj07YuxpCWlTLYd4AXAOggQcIW4YagmZJ1s6fx+JHiOK8zRrbEni+oXNDG8AcA6CBByhNRhV3NTj76ahDUn6xPQRidvPb6pVNBa3sBoA6D+CBByh0bSHhN/r7HM2evOJaZXK2b87VWNnROt2cPYGAGcgSMARUna1LHD2ORu9KcvP0UenDE9cP/evGgurAYD+I0jAEdy2PXZvzps5MnH7tapGNXSE+3g0ANgDQQKOYP5PdXiReyZams2fUK4R+/9ssbih5zfts7giADg8ggQcoaEzGSSGuWjFhpnP69G5x41KXK96ey97SgCwPYIEHMHcIzGs0J1BQpLOO25kYk+JnU1BvVHdbGk9AHA4BAk4QkNHco6Em4PE2NJ8LTId5PXEm3ssrAYADo8gAUeoz5IeCUlaOmtM4vZr2xq0pyVkYTUA0DdbBok///nPOuecczR79mxdeOGF2rBhg9UlwWKpQxvuXLXR46SJ5RpfFpAkGZKefoteCQD2ZbsgsWvXLt1yyy1atmyZ1q9fr//4j//Q1VdfrfZ2zh/IVrG4oaYsmGzZw+vx6N9NvRLPvFOjUCRmYUUAcGi2CxJ79+7VxRdfrPnz58vr9eq8886TJO3YscPawmCZllBEMdPiBbcPbUjSkmNHKeDv/ufZEoqyFBSAbfmtLuBA8+fP1/z58xPXGzduVDAY1MSJE/t8Xnl5wSBXdng+X/cHvx1qcZLDtVttV/Kn8UCOV+NGFrtuZ8sDlUu6cPZYPfbGLknS/765R/9x6mR5vck/N++39NBu6aHd0pMN7Wa7HgmznTt36tprr9X111+voqIiq8uBRerauhK3K4vyXB8ielx+8kT1/FGr6jv08hZOBQVgP7brkejx9ttv64orrtBnPvMZffGLXzzs45uaOoegqr71JE471OIkh2u3nbVtiXD12fcAABkeSURBVNtlgZysad9Sn0cfmzpcr3xQL0n6n79s1ZxRyUDN+y09tFt6aLf0OKXdKiuL036uLXskXnvtNV122WW64YYbdO2111pdDiyWTSs2DnTp3HGJ2xs/bNU7e1otrAYADma7ILFjxw5dd911+s53vqOlS5daXQ5sIGV77CyYaGl2/JgSnTCmJHH96/W7LawGAA5muyDx29/+VsFgULfeeqtOPPHExK/169dbXRoski3bYx/K5+YleyVe+aBeu5qCFlYDAKlsN0filltu0S233GJ1GbCRbA8Sp04ZpqPK87WzKShD0m827NatHz/a6rIAQJINeySAA5nP2RiehUHC6/Hos6a5Es/+q0b7TCtZAMBKBAnYXjbPkehxzoyRGlHU/WePxAw9ylwJADZBkICthSIxtYaiiethBdm1aqNHrt+rz80bn7j+/dt7Vd9OrwQA6xEkYGt17cneCK9HGl6UZ2E11jp/5ihV7A9SXdG4fvnXHdYWBAAiSMDm9pl+6h5WmCu/Nzt2texNIMeXsq/Eb96oVqNpIioAWIEgAVurNU0qHJHFvRE9LjphjEoD3YutOsMxPbRuh7UFAch6BAnYmnl1wohigkRBri9lBccjr1erORjp4xkAMLgIErA18xyJnlUL2W7prDEq2d8r0d4V1SP7TwgFACsQJGBr5jkSDG10K8rz63OmXoknNu5JGQICgKFEkICt1TK00atLZo9NtEdXNK6f/99OiysCkK0IErA189BGJUMbCYEcn649fUri+rl/1WhHg72PKQbgTgQJ2FY0Fk85Z2MkPRIp/n3OOB01rECSFDek+1+tsrgiANmIIAHbqu8IyzBdVzJHIkWOz6sbTId3vVbVqL9VNVpYEYBsRJCAbZnnR5QG/Mrz83Y90NnHjdKJ40oT1z94ZavC0biFFQHINnwyw7ZqWpNBYlRJwMJK7Mvj8eimM6aoZ8PPXc0h/WYDB3oBGDoECdjWntZQ4vboEoY1DuXoyiItnTUmcf3z/9upqoYOCysCkE0IErAtc4/EmFJ6JPpyxckTE6taIjFDy5/fomjcOMyzAGDgCBKwLXOPBEMbfSsO+PX1TyQnXm6qbdfDb1RbWBGAbEGQgG3tbUkGiTEMbRzWKZOHacmxIxPXK9ft1PrqZgsrApANCBKwJcMwVGNatTGaHol+ueH0KYn5JHFD+sYfNqUcfAYAmUaQgC01dkbUZVrGSJDon6I8v7573gzl+rqXcTR2RnTzs+8pGIlZXBkAtyJIwJb2muZHFOX5VLz/tEsc3jEji3XTGVMT1+/WtOnrqzcx+RLAoCBIwJb2tjKsMRDnHz9aF50wOnH916pG3f3i+zIMwgSAzCJIwJZSJ1oSJNJx0xlT9bGpwxLXz/6rVj9+dTthAkBGESRgS7uag4nbo9lDIi0+r0ffPHu6Thxbkrjv0fW79eDfWRYKIHMIErAlc5AYX5ZvYSXOFsjx6fvnH6ujKwsT9/3P33bqMbbRBpAhBAnY0q6mZJCYUE6PxECUBHL0k3+fqaPKk4Hsh3+p0jPv7LWwKgBuQZCA7YQiMe1rDyeux5fTIzFQFQW5WrH0+JQzS779wgd6YfM+C6sC4AYECdjO7ubkRMscn0ejiumRyISRxXn66dLjNbyw+0wOQ9Kdz2/Rq9sarC0MgKMRJGA71ab5EWNLA/L1nJGNARtXlq8VS2eqdP++HLG4oduee09v7GyyuDIATkWQgO2Y50cw0TLzJg8r1E/+faYKc32SpHDM0I2r3tX7+9otrgyAExEkYDspQYL5EYNi+shi/ejC4xTwd38EhKJx3fTMu2oORiyuDIDTECRgO+ahjQkEiUFzwthS3XPeDPWMHO1p7dI32EobwBEiSMBWDMNQVX1H4vqo8gILq3G/RZMqdPUpkxLXb1Q364G/brewIgBOQ5CArTR2RtQSiiaupwwnSAy2z88bp49/pDJx/cg/duv1HY0WVgTASQgSsJVtpt6IioIclRfkWlhNdvB4PLrzkx/RpGHJ0Lbs+S1q7Az38SwA6EaQgK1sa+hM3J48vLCPRyKT8nN8+s45xyjX1z1horEzouXPb1GcA74AHAZBArZinh8xZRjDGkNpamWhrv/olMT1/+1o0v/+80MLKwLgBAQJ2Mq2enokrLR01midNiV59PiPX92uLbXsLwHg0AgSsA3DMFTVQI+ElTwej+446yOqLOqemxKNG7r9j5sUjMQsrgyAXREkYBsftoTUEU7+hzWFHglLlOXn6K5/m6aejcl3NAb1w79ss7QmAPZFkIBtvFfTlrg9oTxfRXl+C6vJbvMmlOvz88cnrn//do1e/qDewooA2BVBArbxXk1yLP6YkUUWVgJJuuLko1L+Hr79wvuqbeuysCIAdkSQgG1sqk32SMwYVWxhJZCkHJ9X3zrnGOXndH9MtIaiWvb8ZsXYQhuACUECthCLG9psWh0wYyRBwg4mlOfrpjOmJq437GrRynU7rCsIgO0QJGALVfUd6ty/MsDrkaYxtGEb5x47Up+YltxC+8G/79JL79dZWBEAOyFIwBberG5O3J40rED5OT4Lq4GZx+PR1z9xtCZVJJfjLn9+i7bsY38JAAQJ2MTr2xsSt2eNLbWwEvSmKM+vez81Q0V53QEvFI3ruqff0a6m4GGeCcDtCBKwnGEY+vv25GmTc8eXWVgNDuWoigJ9+5xjtP84DjV2RnTNU29rHys5gKxGkIDldjZ2qrY1+Z/R7PH0SNjVyZMqdMdZ0xLXe1q79KX/3ahqeiaArEWQgOVer0r2RkweVqAKjg63tXOOHakbTk8e7tUTJt41bSgGIHsQJGC517Ymd0ycPY7eCCf49OyxKWGisTOiL/3vRq16e6+FVQGwAkEClgpFYnrNtPXyKZOH9fFo2MmnZ4/VXf82TT5v96SJSMzQt1/8QHf8cbNaghGLqwMwVAgSsNQb1c3q3H9QV0GOT3MnMNHSSc6eMVI/uWimKgpyEvf9adM+XfLwBr26raGPZwJwC4IELLXWNKxx8qRy5fl5SzrN3AllevTS2Tp+TEnivvqOsG5c9a6+vnoT53MALsenNiwTisT0ygfJn1o/OnW4hdVgIEYU52nl/ztB1546Sbk960MlvbilTkt/9Q899PdqhaNxCysEMFgIErDMyx/Uq60rKkkqyPXp1CkVFleEgfB5Pfr8/PF69HOzdazp0LVgJK4Vf92hTz/SPdxhGBz6BbgJQQKWWfVOTeL2OTNHqzDXb2E1yJTJwwr14Gdm6RufOFpl+cm5E9VNQd246l198fG3tGFXcx+vAMBJCBKwxKbaNr25uyVxfcm8cRZWg0zzejw6//jRevryubp41hh5k6Mdemdvq6584m1d/eTbem1bA8eSAw7Hj4CwxMp1OxO3jxtTouPHlqq5md0R3aYkkKObzpyqT80cpRV/3a5125sSX/tHdbP+Ud2sMaUBfXJ6pU6ZPEzHjCqW35w69ovFDe1tDWlXc1DVjUFVN3X/2t0SVFsoqmAkpmjcUEGuT8V5fo0ry9ekigIdXVmo2ePLNL4sII/n4NcFMHAewyUDlnV11u+qV17efTpiU1OnxZXY28bdLfrSb99KXK+8dLbOmD6CdjtCTny/vbm7RT/963Zt/LC116/n+DwaX5av0oBfeX6fOsIxtYQi2tMSUnQAPRfDC3M1Z3ypTppYrrNOGKMRxQFHtZsdOPH9ZgdOabfKyuLDP+gQCBIZ5JQ3jJVCkZg+++g/E2czzBhVrGe+crI8Hg/tdoSc+n4zDEMbdrXoiY17tHZrvawY2Zg+qlhzx3UHi1ljS1l23A9Ofb9ZzSntNpAgwdAGhoxhGPr+K9sSIcIj6cbTp9DlnGU8Ho/mTijT3AllqmkN6c/v1+tvVQ1688PWw86XGFmcpwnl+Ylf48vyVVGYq4Icn3xej4LhmBo6w9rZFNT2hg699WGrqhoO/gDfXNOmzTVt+vX63crzezV7f6hYOLFCEyvyeU8CR4AeiQxySvK0gmEY+p91O/Xg69WJ+z47Z5y++rHJtFua3NZuXdG4djR2ak9LSO1dUXVF44k5D6NLAxpXGlAgx3fEr9vUGdY/d7fo7zub9PqOJu1t7XuDrJHFeTrpqHLNGleij1QWadKwAuX46LFw2/ttqDil3Vw3tPHWW29p2bJl2rFjh2bMmKF77rlHEyZM6PM5BAn7auoM696Xt+nFLXWJ+2aOLtFPl85UIMdHu6WJdjtyhmGoJS699kG9Xt5Uq/XVzQodZqMsv9ejScMKNKE8X2NKAhpTGtDwwlwVB/wqyvOrYH+4MSTFDUOhSEztXTF1hKMpv7d3RdUejqqjK6Z2033BSEyxuKGY0T2pNG4Y3ddxQz6vp/uXxyO/L/X3XL9XOT6vcn0e5fq8/br27389j8cjr7p7h7weda+q8Xjk80jFeX6V5ueoJOBXaSBHFQU58vu8vN/S5JR2c1WQ6Orq0plnnqlbb71Vixcv1sqVK/Xyyy/rd7/7XZ/PI0jYS9wwtLm2XS9uqdOqd/aqvSuW+NrEinz9/P/NUtn+8xlot/TQbukxt1s4Gtfbe1r1fzua9PedTdqyr93i6uzH6+merDq+okBjyvJVEfBrVHGeRhbnaURRniqLc1WWnyMvw0G9csq/U1cFibVr1+ree+/V6tWrJUmxWEwnnXSSHn/8cU2dOvWQz8t0kKhv79Km2nYZklJbKHnRc3/PPUVFeTIMQ+0d4ZQHmJ9ufi0jcV/vfwUHvn737YO/f+/P6f1xxkE3en9s6vc8+BuY74sbUlsoqpZQRC3BiHY1h7StvkMd4ZgONGd8qb67ZIZKTRsVOeUfmt3Qbunpq90aOsL6+84mra9u1pZ97apq6BzQapFs4fd6NLwwV5VFuSoJ5Kgw16eiPL+K8rp/z/F194b4vR7l+Dzye/df+zxKxA9TEDFHEs/BX07ca77PrjGmqChPktTenrkzZ6aNKNKI4ryMvZ7kssmW27dv1+TJkxPXPp9P48ePV1VVVZ9BoufDIROq6tp1/i//oS7OBsiYojy/vnrmVF26YIL8B4w3+/ZfZ/LvMBvQbunpq93Kyws0dVyZPruo+zocjauqvkNbatq0uymoXU2d2t0UVFNnWK2hqNpCUXWGu7d593i6/1PM3z+voyjgT/09z6/iQE73kIjpa0W5/u7hCtMwRs/tuCFFY3FF9w91RGLx/b933+6KxhWOxhWOJX/vihx4HTNdGzIMQ3Gju9fQMLp/kIgbUjxuKBo31BqMqDkYUXNnRMHIwT8M9CYaN1TT1qUaDmgbEjk+j1ZddbKmjUr/P/9Msl2Q6OzsVCAQSLkvPz9fweDQbVa0ra6DEJEBXo80e0K5PnnsSF00e5yKA7Z7uwF9yvV7NX1Usabb5AN7qAXDMdW2hrSnJaS9rSHtaQ5qd2NQHzYHVdsWUm1rlzp76XnE4IrEDH2wr50gcSj5+fkKhUIp9wWDQRUWFvb5vEx2784aUaiLThitjR8mt3A2dcCpt6FAjyTf/rXo8ZiRcv+hntN9vyflcQc+trfOvt6+fsguvv6+/hF0K5rboudmcZ5fpYHuSVojinI1ZXihpgwvVFFe91ssGgyrKRhWb+iiTw/tlh7a7ciU+jwqrcjXyVOGSUptN8Mw1BGOqa49rLr2LtV3hLsnlZoml7Z3xRTZ36sSjRmKxvff3n8tHTj8axpq7aWeww3f2k1PD1gslrkfTo8fU6L5Y4oz+h521dDG5MmT9fvf/z5xHYvFVF1drUmTJg1ZDX6fV7d+/Ogjfh4fUACyicfj2T8Xwq9Jwxhi6002/L9gu8XRCxYsUENDg1atWqVwOKwHHnhAEyZM0JQpU6wuDQAAHMB2QSIQCOhnP/uZHn30US1YsEDr1q3TfffdZ3VZAACgF7Yb2pCk4447Tk8//bTVZQAAgMOwXY8EAABwDoIEAABIG0ECAACkjSABAADSRpAAAABpI0gAAIC0ESQAAEDaCBIAACBtBAkAAJA2ggQAAEibxzDsfAArAACwM3okAABA2ggSAAAgbQQJAACQNoIEAABIG0ECAACkjSABAADSRpAYgMbGRn3pS1/SiSeeqMWLF+vVV1895GNXrFihj370o5o3b56uvPJK1dTUDGGl1nvrrbd0/vnna9asWfrMZz6j6urqgx4Tj8f17W9/WwsWLNDChQu1cuVKCyq1l/60W1tbm2666SaddNJJWrRokb71rW8pHA5bUK199KfdzG666SbdeuutQ1SdffW33X75y1/qlFNO0bx583TjjTcqFAoNcaX20p92i0QiuuOOO3TSSSdp4cKFuvvuuxWPxy2odhAYSNvVV19t3HXXXUZXV5exdu1aY+7cuUZdXd1Bj3v22WeNs846y9i9e7fR1dVlLF++3LjsssssqNgaoVDIWLRokfHcc88ZXV1dxo9//GPjggsuOOhxDz30kLF06VKjqanJ2LFjh3H66acbL730kgUV20N/2+322283rr32WqO9vd1oaGgwLr74YuOBBx6woGJ76G+79XjxxReN6dOnG7fccssQVmk//W23P/zhD8aZZ55pVFdXG+3t7cZll11m3H///RZUbA9H8vl2+eWXGx0dHUZDQ4Nx9tlnG7/73e8sqDjz6JFIU0dHh1555RVdc801ys3N1WmnnaY5c+ZozZo1Bz22paVFV1xxhcaOHavc3FxdcsklevPNNy2o2hqvv/66ysrKdO655yo3N1dXXXWVdu3apa1bt6Y8bvXq1br88stVVlamo446SpdeeqmeeeYZi6q2Xn/bzTAMXX311SosLFRFRYXOPfdcbdy40aKqrdffdpO6exXvvfdeXXjhhRZUai/9bbcnn3xS11xzjcaPH6/CwkJ973vf0wUXXGBR1dbrb7vt3LlTsVgs0Qvh9XqVl5dnRckZR5A4jGg0qtbW1oN+bd68WcXFxaqoqEg8dtKkSdq+fftBr3HppZem/ENbu3atpk2bNiT128H27ds1efLkxLXP59P48eNVVVWV8riqqqqUx02aNOmgx2ST/rbbt771LU2fPj1xvXbtWn3kIx8Zsjrtpr/tJkl33XWX/vM//1OjRo0ayhJtqb/ttmnTJrW1tWnJkiVatGiRfvrTn2rEiBFDXa5t9Lfdli5dqi1btmjevHlauHChJk+erLPPPnuoyx0UBInDWLdunebNm3fQrx//+MfKz89PeWwgEFAwGOzz9V566SU98MADuummmwazbFvp7OxUIBBIuS8/P/+gtgoGgyltGggEsnrstb/tZnbvvfeqqqpKl1122WCXZ1v9bbc//vGP6ujo0NKlS4eyPNvqb7u1trZq1apV+vnPf67nnntO7733nn72s58NZam20t92C4fDWrJkiV5//XW99NJL2rZtm37zm98MZamDhiBxGKeddpq2bNly0K+bbrrpoP/kQqGQCgoKDvlaTzzxhG6++Wbdd999mjNnzmCXbhv5+fkHtVUwGFRhYWHKfQcGh8O1p9v1t92k7p6zb3zjG1qzZo0eeughlZeXD1WZttOfdquvr9d///d/65vf/OZQl2db/X2/5eTk6HOf+5xGjRqliooKXX755XrllVeGslRb6W+73XbbbTr33HNVWlqqcePG6corr9RTTz01lKUOGoJEmo466ii1tbWpubk5cd/27ds1adKkXh//k5/8RPfdd59+9atf6aMf/ehQlWkLkydP1o4dOxLXsVhM1dXVB7XVgY/rqz2zQX/bLRwO66qrrtL777+v3/72t5owYcIQV2ov/Wm3v/3tb6qvr9eSJUs0d+5crVy5UqtXr9aSJUssqNge+vt+mzhxotrb21MeZ2Tx2Y/9bbeamhpFIpHEtd/vl9/vH6oyBxVBIk1FRUU69dRT9cMf/lBdXV167bXXtH79en3iE5846LHPPfecHn30UT322GM6/vjjLajWWgsWLFBDQ4NWrVqlcDisBx54QBMmTNCUKVNSHnfOOedo5cqVamhoUHV1tX79619n9Qd7f9vtm9/8plpbW/XII49o2LBhFlVrH/1pt0996lPauHGj1q9fr/Xr1+vLX/6yzj33XD333HMWVm6t/r7fPvWpT+mxxx5TbW2tGhsb9eCDD2rx4sUWVW29/rbbqaeeqh/96Edqa2tTXV2dfv7zn+uss86yqOoMs3rZiJPV19cbV111lTFnzhxj8eLFxtq1axNfu+OOO4w77rjDMAzDuOCCC4wZM2YYs2bNSvmVTd555x3jwgsvNGbNmmV8+tOfNnbu3GkYhmGcffbZxjPPPGMYhmFEIhHjnnvuMU4++WRj4cKFxsqVK60s2RYO126tra3G9OnTjeOOOy7lvfXFL37R4sqt1Z/3m9n999+f9cs/DaN/7RaLxYwVK1YYH/vYx4y5c+cay5cvN7q6uqws23L9abempibjxhtvNBYsWGAsWrTI+P73v29EIhEry84Yj2FkcZ8UAAAYEIY2AABA2ggSAAAgbQQJAACQNoIEAABIG0ECAACkjSABAADSRpAAAABpI0gAAIC0ESQAAEDaCBIAACBtBAkAAJA2ggQAAEgbQQIAAKSNIAEAANJGkAAAAGkjSAAAgLQRJAAAQNr+PznH0OY0g9iHAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 600x450 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data['换手率(%)'].plot(kind='kde')"]},{"cell_type":"markdown","metadata":{},"source":["### 67.计算前一天与后一天收盘价的差值"]},{"cell_type":"code","execution_count":88,"metadata":{},"outputs":[{"data":{"text/plain":["0         NaN\n","1      0.1413\n","2      0.1237\n","3     -0.5211\n","4     -0.0177\n","        ...  \n","322   -0.0800\n","323   -0.1000\n","324   -0.0600\n","325   -0.0600\n","326   -0.1000\n","Name: 收盘价(元), Length: 309, dtype: float64"]},"execution_count":88,"metadata":{},"output_type":"execute_result"}],"source":["data['收盘价(元)'].diff()"]},{"cell_type":"markdown","metadata":{},"source":["### 68.计算前一天与后一天收盘价变化率"]},{"cell_type":"code","execution_count":89,"metadata":{},"outputs":[{"data":{"text/plain":["0           NaN\n","1      0.008988\n","2      0.007799\n","3     -0.032598\n","4     -0.001145\n","         ...   \n","322   -0.005277\n","323   -0.006631\n","324   -0.004005\n","325   -0.004021\n","326   -0.006729\n","Name: 收盘价(元), Length: 309, dtype: float64"]},"execution_count":89,"metadata":{},"output_type":"execute_result"}],"source":["data['收盘价(元)'].pct_change()"]},{"cell_type":"markdown","metadata":{},"source":["### 69.设置日期为索引"]},{"cell_type":"code","execution_count":96,"metadata":{"scrolled":true},"outputs":[],"source":["data = data.set_index('日期')"]},{"cell_type":"markdown","metadata":{},"source":["### 70.以5个数据作为一个数据滑动窗口，在这个5个数据上取均值(收盘价)"]},{"cell_type":"code","execution_count":91,"metadata":{},"outputs":[{"data":{"text/plain":["0           NaN\n","1           NaN\n","2           NaN\n","3           NaN\n","4      15.69578\n","         ...   \n","322    15.14200\n","323    15.12800\n","324    15.07000\n","325    15.00000\n","326    14.92000\n","Name: 收盘价(元), Length: 309, dtype: float64"]},"execution_count":91,"metadata":{},"output_type":"execute_result"}],"source":["data['收盘价(元)'].rolling(5).mean()"]},{"cell_type":"markdown","metadata":{},"source":["### 71.以5个数据作为一个数据滑动窗口，计算这五个数据总和(收盘价)"]},{"cell_type":"code","execution_count":92,"metadata":{},"outputs":[{"data":{"text/plain":["0          NaN\n","1          NaN\n","2          NaN\n","3          NaN\n","4      78.4789\n","        ...   \n","322    75.7100\n","323    75.6400\n","324    75.3500\n","325    75.0000\n","326    74.6000\n","Name: 收盘价(元), Length: 309, dtype: float64"]},"execution_count":92,"metadata":{},"output_type":"execute_result"}],"source":["data['收盘价(元)'].rolling(5).sum()"]},{"cell_type":"markdown","metadata":{},"source":["### 72.将收盘价5日均线、20日均线与原始数据绘制在同一个图上"]},{"cell_type":"code","execution_count":93,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12c17d490>"]},"execution_count":93,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAg4AAAGFCAYAAACVJHu/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOydd3wUdf7/XzOzNb13IIHQpYTepCrYURHF7nl64J3e19M72+/O807vLGdHT/T0PA/FdoooFhRBkU5CqEIChADpyabtZvvM/P6YZEp2N9mQXUjI+/l48HjMZ+Yzs5+dLDuvfVdGFEURBEEQBEEQQcCe7QUQBEEQBNF7IOFAEARBEETQkHAgCIIgCCJoSDgQBEEQBBE0JBwIgiAIgggaEg4EQRAEQQQNCQeCIAiCIIKGhANBEARBEEFDwoEgCIIgiKAh4UAQBEEQRNCQcCAIgiAIImhIOBAEQRAEETQkHAiCIAiCCBoSDgRBEARBBI3ubC+gO3i9PBoa7Gd7GT2O+PgIAKB74we6Nx1D9ycwdG8CQ/emY3rq/UlOjj6t88jiQBAEQRBE0JBwIAiCIAgiaEg4EARBEAQRNCQcCIIgCIIIGhIOBEEQBEEEDQkHgiAIgiCChoQDQRAEQRBBQ8KBIAiCIIigIeFAEARBEETQhEw4vP3223jooYcAACtWrEBeXp78b8yYMRg6dCh2797tc15xcTGGDx+umb9u3bpQLYsgCIIgiBDS7ZLTPM/jrbfewgsvvICFCxcCAJYtW4Zly5bJc5588klUVVVh3LhxPucfPnwYs2fPxmuvvdbdpRAEQRAEEWa6LRwefPBBWK1WLF68GG632+d4YWEh1q5di6+++srv+YcPH8awYcO6uwyCIAiCIM4AjCiKYncuUFNTg5SUFCxfvhzl5eV46qmnNMeXLFmChQsX4vrrr/d7/i9/+Uu43W6UlZWBYRhcd911WLp0aVCvLYoieF7ozvLPSThO8kDRvfGF7k3H0P0JTG+4N6IootnpRaxZf0Zftzfcm7NJT70/Oh13eud194VTUlICHtu9ezfKysqwaNGigHPi4uIwcuRILFmyBBUVFVi6dCmSk5Nx9dVXd3dpBEEQfQYvL+Ca17fjUJUVD100FL+Yln22l0Sco4S1rfaaNWtw+eWXw2AwBJzz3HPPydu5ubm48cYbsWHDhqCEA88LPa5NaU+gp7Zw7QnQvekYuj+B6en35rN9lThQ0QwA+NtXh3Hl8MA/6kJNT783Z5ueen96ZFvtTZs2Yf78+QGPO51OPP3007BarfI+t9sNo9EYzmURBEGccxypbTnbSyD6CGETDnV1daitrcXIkSMDzjGZTNi8eTNeffVVeDweFBcX47333sMVV1wRrmURBEGck9g9/NleAtFHCJtwqKioQGxsrF83RV5eHvLz8wEAy5cvR1FRESZPnow777wTy5Ytw6xZs8K1LIIgiHMSRzvhIHQv7p0gAhKyGId77rlHMx49ejS2bNnid25hYaG8nZ2djbfffjtUyyAIguiTtLi1wsHlFWDWn17UPEF0BJWcJgiCOAdwtBMO7S0QBBEqSDgQBEGcA7S3OJBwIMIFCQeCIIhzgAaHRzN2enpWsSHi3IGEA0EQRC9HEEU02rUl/51kcSDCBAkHgiCIXk6TwwO+XRKFgywORJgg4UAQBNHLsdg9PvsoxoEIFyQcCIIgejn1Lb6diUk4EOGChANBEEQvp96PxcHpJVcFER5IOBAEQfRy6u2+FgcKjiTCBQkHgiCIXo6lxV+MA1kciPBAwoEgCKKX48/iQDEORLgg4UAQBNHLaXRQVgVx5iDhQBAE0ctpdnp99rkoOJIIEyQcCIIgehGiKPq0zLb6EQ5kcSDCBQkHgiCIXoLDw+P29/fg4hXbsfNEg7y/yUnBkcSZg4QDQRBEL2H1vkocqLSi3u7Bb/63H4BkgbC6yOJAnDlIOBAEQfQSdp1s1IxFUYTLK8DTvlEFqI4DET5IOBAEQfQSEiMNmrHF7vEbGAkAOncT2OZTZ2JZRB+DhANBEEQP5JtDNbj7f/uw8UidvI9jGM2ckroWv8JhCHMKb1mXInHlVJh+/iDsayX6FrqzvQCCIAhCi6XFjce+PgxeBA5WWTFjYAL0HOsjEkosdug4SUywEPAP/euYwv6MTMYizzEd/hDOEUvO6PqJcxuyOBAEQfQwth6vR1vYgs3Fo6LJCQBobpc9UWJpkVMxL2QLsIj7SSMaAEBfuSv8Cyb6FCQcCIIgehgFZU2acXmrcGifPVFSZ5etEAu5LQGvxzgbAh4jiK5CwoEgCKIHIYqipkYDAJQ1tlkcfF0VzU4vomHHPLYw4DW5hmOhXyjRZyHhQBAE0YM4Xm9HrU3btKq8yQHA1+JgdXlRYmnBRdxOGBnJjWERozHH9RyKhCx5HtdIwoEIHSQcCIIgehC7TjT67KtockIQRb+lpY/W2XEJu0Mef8lPwXExHQfEbHmfjoQDEUIoq4IgCKIHUVpv99lX1uiEzeWFAW48oPsQ/ZkarOLnYqOQh0pLI6awh+S53wiTAAAlQgbASfvIVUGEEhIOBEEQPYhqq8tnX3mTA81OL/6sewc36DYCAC7kCrCan441/HSYOcm14WHNOMANBwTgmJghn0+uCiKUkHAgCILoQfgTDpcKG5H72YPI053Q7L+K24KrVNkUVQkTwdQZAI8XJWK6vJ9rOgEIXoClr3yi+1CMA0EQRA+ipl1g5BDmFJ7Vv47olhMBzlCoT5kBs17yT5wQUyGIUnEoRvCAtZaFfrFEn4SEA0EQRA/B6eHR6FCKPCVE6DGH3aOZUyPG4WLXkzghpPicb8uahaw4EwDABQPqECsf41qqwrRqoq9BwoEgCKKHoLY2sAwwoV8cprEH5X35whDMdj2PQ+IA3OW5FzVinHzsG34iDIk5uHvmQOV6quNsS02YV0/0FcjhRRAE0UOoUcU3JEUacPuENIw5USTve9Z7LewwwaRj8bM3G1NcryCLqYUZLhwVM/GVSYfshAg8c8UIPPD5z6gW43EeSgEArJ2EAxEayOJAEATRQ1AHRqZGGzFMKIYZ0j6nqEehkAsAyE6IAAAIYHFSTEWR2B8COMQYpd+CBp301a61OJCrgggNJBwIgiB6CGrhkBJthL5MyZjYJQyFCwYAwIAEs8+5/ePN0HHSV7qhtWNmDeLl4+SqIEIFCQeCIIgeQnuLg+HkD/J4mzBS3h7QanFQMzQlSt42cH4sDuSqIEIECQeCIIgeQo1NEQ7ZJjt01Urjqh+FMfK2Scciyshpzh2WqggHvT/h0FId8vUSfRMSDgRBED0EtcVhlDMfDEQAAB+ZioPiAPlYjEmHhAiD5twhfiwO1aLKVUEWByJEkHAgCILoIaizKgY2bZO33QPm4tczcgAAiZEGzBuSDF4QNeeqXRX6thgHtcXB1QR4HWFZN9G3oHRMgiCIHgAviGhu7X7JgUdijRIY6R4wD7fl9MOFQ5ORFGmASc+h1qYtTR1n1svbxtasijrGjB9MZvxsMiBCEDCl/jASU/LOwLshzmVIOBAEQfQAbC4v2mwImUwddO4mAIAIBp6sGWAYBllxSjZFRqwJpfX+LQgsK8CY8gX0cfm4h0uW93P59+DOYb/GtQNvCNv7IM59yFVBEATRA7C6vPJ2JlMnbwuRKRANUT7z7501SN5+/JJhmmMbqtbCkLgFDKe1SvAQsOLwK9hdlx+qZRN9EBIOBEEQPYA2NwUADDI0yNtCVKbf+dNy4vHmkjFYce1oLBimWBV4wYs1Jz/UzJ3gcEIvKjER/9j/d9i9LaFaOtHHIOFAEATRA7CqhMMArl7e5qP9CweGYTAmMxbj+8WBYRh5/w+VG1DtrJTHy0qz8HZVDd6rqAIHaV61owqrS/8X6rdA9BFCJhzefvttPPTQQwCAFStWIC8vT/43ZswYDB06FLt37/Y5z+l04v7778eECRMwa9YsrF69OlRLIgiC6DU0OZWumFmsIhyEqIygryGKIj4oeU8ee5ryAE8SAGC424ObOOVan534BB7B43MNguiMbgsHnufxxhtv4JlnnpH3LVu2DIWFhfK/JUuW4KKLLsK4ceN8zn/++efhcDjw008/4ZVXXsFTTz2Fw4cPd3dZBEEQvQp1jEO6KsYhkMXBH/l1O3DMekQeuy2z0Cgq8RE38JHQs1L2hcVVhx8qv+/Okok+SreFw4MPPoiCggIsXrzY7/HCwkKsXbsWf/3rX/0eX7t2Le666y6YzWaMGjUKl112GT7//PPuLosgCKJXoY5xSBVq5e1AMQ7+eL/kXWVgHwbBlYZGKMIhyWXDvIz58vjT0o9Oc7VEX6bb6ZgPPPAAUlJSsHz5cpSXl/scf/rpp3H33XcjNjbW51hTUxMsFgsGDlT6x+fk5GDz5s1BvTbHsYiP963Z3tfhWqvG0b3xhe5Nx9D9CUy4740HbXEKIhJ5RThEZg5CZBCveaThCPZYFHew0XYBrACaxEh5n95rxW2jb8E3ZV8CAIqaDqMWZRgSP6Rba6fPTceca/en2xaHlJSUgMd2796NsrIyLFq0yO9xh0PKQTabldxkk8kEp9PZ3WURBEH0KhodUrxBHGwwiKrvwJisoM7fULZB3h6eMAKm1hbcTSpXBRwNGBo/FCMTlYZZa4591o1VE32RsBaAWrNmDS6//HIYDAa/x00mEwApQDIiIsJnuzN4XkBDgz00iz2HaFO1dG98oXvTMXR/AhPue1PXLImFTMYi7xN1ZjQ4jYCr89fceGKjvD09aSZWt2ZaNEKxOMDRgIYGO+anX4qDloMAgLUlX+KW7F/BwPn/ng4G+tx0TE+9P8nJ0ad1XljTMTdt2oT58+cHPB4XF4eEhASUlpbK+44fP46cnJxwLosgCKLHYW3NqshoHxipSrUMRJ2zFkVNSlD51NQZcqMrdXAk43UAvAtz0i+AkTUCAJo9TSio2xWS90D0DcImHOrq6lBbW4uRI0d2OO/SSy/Fyy+/DJvNhgMHDmDt2rW47LLLwrUsgiCIHklbcGSGyuIQbGDkdlVfi3RzBrKjcmBo7VehDo4EAMbZhCh9FCanTJP37ajdetrrJvoeYRMOFRUViI2N9eumyMvLQ36+VPL0vvvuQ3x8PObNm4e7774bDz/8MIYPHx6uZREEQfRIFOGgtjgEV8NhU9UP8vbU1OlgGAb6VouDCwZ4W60LAMC6GgEAU1TCYXvNVoiittsmQQQiZDEO99xzj2Y8evRobNmyxe/cwsJCeTsiIgJPPvlkqJZBEEQ3qG9x4/7/7YPN7sZjFw9DRqzpbC+pz9BWxyGzixaHepdF03vi/NTZAAADp7g4XLpo6NxS3wrGKQmHSclT5OM1zmqU2o4jJ1rJcCOIQFDJaYIgZF78/gh+OlKHwvJmPPZN0dleTp/gQGUzbl9ViBY3D8BPjEMnbKhYDwECACDFlIpRCWMAQLY4AICTi5G32ywOCcZEDIlRmmPtqN3WjXdB9CVIOBAEIbNq5yl5u7Cs6SyupO+wYksp9lda5bE2xqFzV8X68nXy9gWZC8Ay0te6Uad8vTtUwoFxKX9XtbtiZw0JByI4SDgQBCGj5zqP4CdCy44TjfK2Hl6kQBl3ZnEoajyE4mYlm+KCjAXKtVQWBzunpN2xTuX6k1Omytv7G/bC5rF1cfU9A6eHx/Mbj+GJdcWaZmFEeCDhQBCEjFHHne0l9GlSmXqwjBKkKESldzj/w+Or5O0RcSORHa2ksqtjHOysKiXTpQiHIbHDEGuIAwDwIo+Cup2nv/izyDeHavD+7nKsOVCFv64jF1u4IeFAEISMSU9fCWcatZUnE4qbgo9IATijv1MAABX2cmyqVIo+XTvwxnbXVf6WNta/xYFjOExMmiyPe2ucw9bSBnn7h6MWVDVT9eFwQt8SBEHIqP3iRPjhBREeXrEwqAMjO4tv+N/xD+WgyMyILExPPV9z3KAWDowiHNQWB0DrrthZux2CKHThHfQM4szaBMEPdlecpZX0DehbgiAIGZOeXBVnEntrJkUbmsDIDuIbmtxN+KZsrTxenHM9OEb7t1NbHKyM4qpg2wmHiUlTwLY+CupdFhy3lnThHfQMXF6t2FlzoBJegepShAsSDgRByBjaWRwEKgoUVlrc2kC+K/p75G2+gxoOn5/4FE5eMsfHGuKwIOsSnzlGneICaVYJB8apFQ4xhhjkxijdMffWF6K30V6A2Vw8am2us7Sacx8SDgRByLR3VbT/QiZCi011f406Fjl6xVcvBKgayQtefHbiE3l85YBFMPqJhVBbHJpVZafZdsIBAMYkjpW39/VQ4SCIIgpONeLnKqvPsRY/n9Nam/tMLKtPQsKBIAgZU7usCn9fyEToaHEpFodIAwfWqvjmA6Vi5tftQoO7HgCgZ/VY2P9qv/PUMQ5N8J9V0cbohDx5e1/9nh5ZfnrtgWos+2gfbn2vEPknG7G3vAnVVsmq4E/gksUhfIS1rTZBEL0bm8uL1OjAkf1E97B7lAdelIEDZy2Tx4HKTa+vUAo+TU6ehjhjvN95aotDo6i01mZczYDAA6wiEkcnjAEDBiJENLobccJWqknt7Ak8/m2xvH3Xx/sAAGY9iw9vm6C5j22QxSF8kMWBIAgZD68NMiOLQ3hpcSn3N1NvBeO1y2M+pr/PfIfXji3Vm+TxBZkLfOa0oa7jUC+oLA4QwbibNXOj9TEYGJ0rj3tanEMgC4jDI+BfW0+QxeEMQ8KBIAgZdWog4Bu8R4QW9f3N4WrkbcEYC9EU5zN/a/VmOSgySheNKclTfea0oQ50bRTMEFnFwMw66n3m9+Q4h5oOrAd7yptk4cBAQBKaAIhkcQgjJBwIgpBpb3GwucjiEE7UFp0BTLW8zcdm+52/peYneXtG2kwYOigQpY5x8AgiBFOCPGacvsJBHeewt4fFORTV+C+FzUKA3dECu9sLBgJW6F9Evuku/Ev/POqtLWd4lX0HEg4EQci427sqXGRxCCdq4ZAlqoRDzACfuR7Bg52qyo7TU2d2eG11jIObFyCalFgI1tngM39MgmJxqHdZUNZyymfO2aKo2lc4DGNOYr3h99iBW7GQ2YSL2F1YwEntxS/kCnB54ztnepl9BgqOJAhCpr2rwkYxDmFFHeOQxlfK2/4sDnsthbC3xkAYWSPGJ03s8NoGVR0HDy9CUAsHP66KWEMcsqNyUGo7Lr1efSH6RfnGWZwN2lscxjJH8V/Dk4hhHACAx/TvwAazZs5N3k/RsqMf9NWF8CYOR8u0PwIMNXELBWRxIAhCxic4kiwOYUUd45CiEQ6+FoetNZvl7fFJE2HiTB1eW+2qcHsFiOaOXRUAMEaTltlz4hy0wkHE3/RvyaIBAGIYBzIY3/cUmf8SDKc2IWLP6zCc2HAGVto3IOFAEIQMZVWcWdT3N8FdLm8L7YSDV/Dix0rlwTetXV8Kf7R3VQhGtasigHBI7HlxDg12N6qsSobEaKYEI9kTAedvF0bAIRp89usrtodlfX0REg4EQcj4uCrI4hBW2iwOUbAj0qsUZmof47Cjdqum6FNn8Q0AYGwvHDQWB98YB0AbIFnrrEGl4+w3i9pXoa0UuYRTBFSJkAanqJfHhzAQf9L/Ho96b/O5jq5mT9jW2Ncg4UAQhAxZHM4sbWmEAxglFVPkjBAiUzXzvjqlNLSakToTsYbYTq+tbxfjIKqyKliHf+GQYExA/0hFtOy1nH13xf5KpeZEKupxBacEiL7ivRJ/9N6OSjEBn/IzcK/pCRhjkvExPwuPem6FU6/cJ13NPqnwFdFtSDgQBCHjKxzI4hBOWmThUCXv42OzAUb5aq53WbBDlU1xcdblQV27fYyDJjgygKsCAMYkjpO399TvDuq1uoPV6cUT3xbj2Q1H4fRTAXJfhSQczHDivcgXEMVIdSyaxAh8JUzG//hZmOp6Bfd5fg3GEIXkKCMABv/lF+DVkZ9AbL2XrKcFXMPRsL+fvgAJB4IgAEjV+XxdFfQLLZy0BZ+OYY/J+/j4XM2cHyo3QBClv0OKKRV5SeODurY6xkEE4DUoBaUCuSoAYKy6noOlMOxxDv/adgJr9lfhw8IKfH6gSnPMywtyU6tf6z5HLq/cp69SlsLLagNEIwwcchIjpAHrxHrLetya1R8LsjJweWY6VhxejlO2k2F9P30BEg4EQQAAvILvA4IsDuGlzeIwkS2S93nStWmW6qDIORkXgGO0jcgCoS45DQBOlXDo2OKgCIcaZ3XY4xze360Ehf57h7Z2RHFtC1xeASwELOaUUtvlA2/A3MX3ITFSGwQZYeBwXnok9PFbEDXoGZTpVqFQJ6BCr0OpQY/3m3bjtk3X4297HoPVoy27TQQPCQeCIAD4BkYCFOMQTkRRhM3Nwwg3zmOOy/vVwqHWWYv9DXvl8Zz0eUFf39CuRbpLr7gqGGdjQH9/gjFRE+ewuy4/6NfsKuVNDs040qAVRXtb3RST2UNIa023FBkOxlkPgGUYP8IBWN/wMkxpX4DR2eEPESK+r/gWfyp4CB7BE6q30qcg4UAQBADf+AaAsirCiZsXwQsixjDHYGCkh7ioM8ObOEKeo7Y2pEdkYHDM0KCvr45xAACnTgkUZCCCcTUFPHecqrjU9potQb9mV9lxQtviu71bZNcJyaWykFXW4O43E2JEEgAgqZ1wKGVX4qfq75Xr8QZE1o/BP6tq8KClAQM8ilDYV78HLx14tkeknPY2SDgQBAFA6mfQHodH8OvCILpPmxtoAqu0i/akjgM4Jb1wa7XSm2JW2lwwXah8qGO1c+1MhLbRVQdxDlNTpsvbBXW74OJD32my4FQjnvzuiGZfldUFofVB7uUF7C5rghlOXMLtlOe4hlwlb6uFAxd5BJWicr+8tly0HPsDqquvw2g7i5uarVhTVokLYsfIc74q+wIvHnwWgugrmonAkHAgCAKA9EXtj+MWahYUDtrKTY9XC4f0CfK23duicVNMD6LokxqGYZASpTxYd5c1ddroqo0xCXkwc1KQoUtwhdxdUVjWhGUf7fPZH8k3g/3p74jIX44Th3aixe3FNdwmxDCS20HQR8KVo7QST4xsE1kCjClfyvvTDIPhKLsVIh8NESwKhCEAAA7An7hsjIwfJc/94uRqPH/gaRIPXYCEA0EQAPzHOABKOhwRWmxuLwBRk1HhSVOEQ6GlAHxrNkW0PhrD4ka0v0SnzBmcJG+vL67VNrry06+iDQNnwMTkyfJ4q6orZyjYetz3tQ3w4L+Gp5C8/zVE7ngaU368BtuM9+Be3SfyHOfwJYAhUh63WRz0CVvAmZSMjF8Puw8cFMtNvqC4eKKqC/HkhOcwIu48ed9Xp77AU3sfp5iHICHhQBAEAP9ZFQCwn4RDWNhT3oxM1CGJUe6vN1XpULmzRimRPCFpUtDZFGouGJIsb+efbIQ7yMwKAJiWMkPe3lG7LaSxADU2resjDla8oH8Vo9njmv3pTD0SGSkdU2RYOMb8UnM8MdII1nQKxpRv5H0XZl6EGVlj8NTlI9CWWJLfanEAAH3VbkRxJjwz6QWMilfcFusr1uGR/N/DyTtD8h7PZUg4EAQBwH9wJEAWh3CxvqgWo1QPSj6mv2wREEURO+sU4TAxecppvcbozBjZXcGLQJVH+bXeUS0H6TUVi0OdsxYnWwL3h+gq1areEy9OasE28+9wqSqOoUxMgiBqYzRcgy6FEKPt1hlhdMKcuQpMa3BpFJeIu4bdAwCYPTgJz145EgCwVxwET2szaMZrh74qHxG6SDw18TnkJSp1MQrqduHRgofgDkNMx7kECQeCIAD4D44EgFONTjTY3Wd4Nec2Vc1O7Ktoxmi2RN7nSR4tb59qOYFqh2J6n5g0GacDyzCYlau4KypVwqEzi0O8MQGDogfL44K6nR3M7hpq4TClciXMopI6uU/IwTzXs5jsWYGXon6HI8kL4Bh+HWyzntRcQxRFfFz+IlhDQ+uYxZWp9yNO1cwrNdoIAHDBgB3CcHm/sfgzAIBZF4EnJzynSXPNr9uJ1w4tD9l7PRch4UAQBABtcGRSpEETsd6+0RDRPTYcqQMAjGIU4eBNUYTDjlrF2pAbMxiJJuXh31Wy4pTqinWCyuIQoF+FmvGqtMz8ul2nvQY1oiiiplU4RMOOFItSTvtLfhKud/8RJlMEVi2bjxtuvR9x174F29znIJriNNfZVrMF+fWqNM3aC5GsG6aZ0yYcAGC1V8kUMR5dC/CSGDZwBjwy9jGNePji1BqctJV2/82eo5BwIAgCgDY40sAxGJkWLY/Zo18h7uNLEfXDQwDlvXeb9UW1AESNxcGbovjbd6mEw8Sk03NTtKEWgFVetcWha8Jhr6UQXqH7dT0aHR64Wz9r89jdYFsDEi1iNH7ruQctMGPRmHTEmfUdXQafn1wtb3tbBsFtmYUhKVGaOdFGHUythbDWCRPAs5KQYF2NMJz8UZ7HMRweGvMoMiOyAACCyOOtoje6+U7PXUg4EAQBAPAIisVBx7FyVb6buW9x9bGHoa/ZC/PBd6EvC22EfV+jstmJ/ZVWjGeKEcsoJnpvspQi6OSd2FuvtICelNI94aCurljujpC3O3NVAMDohLHQs9L5Dt6Ogw37u7UWQOumuEyvuD/W8RPAQwoAXZyX2eE1quyVGnGV4LoIN0/oj6HthAPDMLLVwYYIVCTPko8ZS77WzNWzevxy6FJ5/FP1DzjU+HOwb6tPQcKBIAgAWouDnmMQY9Ihi6nFo7qVmnm6+uL2pxJdYH1RLRLQjBf1/5T3eZLOg2iMAQDsseyGR5DM6BG6CIyMG+X3OsGiEQ4uxW3RUR2HNoycEaMTFEvIthBUkWwTDpFwYCaj1Klo7H8R4sx6PDgv16ciZHu+PPU5REif16yIflh94w347ayBfueq3RWHYpRMEX3FDp+5M9PmYEiM4u5458hbQbyjvoeu8ykEQfQF1DEOepZFjEmHScwh6BltTwO2+VT7U7vM1uP1eOGHYxiVHoM/LhgCtgsVEXsDoijii4PVWLnrFLLizEiJMmJ3STlmuTfhPLEIG427ZGuDyLBoOf8x+Vz1L+lxiROhY7v3Na1+CNcJKvdTB3Uc1ExLOR8FrfENW2s2Y5OHgOIAACAASURBVNnwu09rHbwg4tGvDuPboloAwFy2EAZIbgrBGIvFV1yLxVzH7gkA8ApefF22Vh5f2n9hhxU11cJhPzcC81u3ueYTYG2VEKLS5eMsw+K2IXfgkfzfAwB21m7DwYb9moJRBFkcCIJopb3FIdqowzDWVyRwIRAOr20uRWm9A18crMbaA9Xdvl5PQhBF/O3bI3h8XTFK6x3YXFKPhgNfYY3nV/gL+wYWcz9qXBT2Sb+HJ0NxR+yqVX4JTzrNNEw1kQZO9vPXQxEOjKspYKMrNVNTlaDCspaTOGk7vbTMTccssmgAgEs45X26ci7SlNruiG01W1DvsgCQ3AsXZV3S4fwUlXBYvseNBoMiFPSVvpkik5OnYkTcSHn8nyNvBrWuvgQJB4IgAPjGOMSYdBjO+D4kOGv3hIMoijhcY5PHb+042a3r9TQ+2VuJNQeUVMpY2LBcv1wjFgDAyZhhnf4o7OPvkfeVt5ShzK7c31AIB0bVRbJRVAmHThpdtZFmTsfA6Fx5fLruiu2lSjBmBJyYwypxHO5BHT/81aw9+Zm8fX7qbMQa4jqYDaSphAMAbHAMkrf9uSsYhsFtg++UxwV1u7BPFXNCkHAgCKIVjcWBZRBj0mMY6/tQZ5tPdSuzwubS/sqtaHLCHsL23ZtLLPjd6gP49nBNyK4ZLGWNDrz8Y4lm3y903yCKUaoRVg28DicnPQ7rrVvgHPsrQGVmV1sbsqNykGJODcm62twVVpghqCpQBhMgCQDTUpXYgJ+qNp7WGoprFbE4i90LE9PqpjDEwN0vuD4c5S1lyFfVk7i8/5WdnjN7cJImQ2Onqp6DP+EASNkk6qqS/yl+k7poqiDhQBAEgPauChYJaEAy41s1kvXYwLgaffYHizqqvo3NJZbTvp6ao3UteODzn7G5pB6PfVOERvuZ7T3wwe5yOL2S5SbWpMO3vzwP90QqbZ5bJt4H7uLnYJ74CyAyxef8UFSL9EeS3OyKgV2n/EJngoxzmJk2W97+ufEgKuzlHc7fW96EJ9YVY0erlcHlFXC4WhEO09iD8rY7ex7AdRwM2cYnpR/JQZH9IwdgdMLYTs4A4sx6fHHnJNw5Vao6uUvVt0JXXwTG5fsZZxgGtw25Qx7vqd+NnarYk74OCQeCIAAAXpWrQs8xSLYrLY9rxDgIOiWVrztxDtU2X+Gw6Vj3hYOXF/CXr4tkAeThRew9w+Wyj1kUd8Stk/ohs+gtcK3uAEEfBcfo2wOe6/DaUajqQhkKN0UbiRHKg9nKxsjbwdRyAIBB0YMxICpbHm+sWB9wriCKeODzn7HmQBXu/mQ/WtxeHK62anqhTGCL5G1P5tSg1mD1NOMbVVDkopzrgm4zbtJzuHPqAOQmRaJETEezaJaP6SyH/J6Tlzge4xOVOhZvHH5VbjrW1yHhQBAEAK3FQceyiLMqwuGQ0B+eqCx5zDafflyCP4tDZXP3ewN8V1yriZ0AznyfjapmxSUxPMKKiD2vy2PHmF/6VD9Us71mK1xCa6qiLiqoX9PBkqRqr92sCpAM1lXBMAzmZlwojzdUfBdwrt3No15l6TlS06L5O0xJZTCMLZPHnrSJCIa1J9fIDahi9LG4MPOioM5rg2EYzB+WDIDBIXGAvJ+rOxjwnKXDfwMGkjg5bivB9+Xfduk1z1VIOBAEAUDb5ErPMTA3qYSD2B+OSEU4cNYynC7+hENnvTA2HbPg070VcHoC/+LLP+nrPtlfeeaEgyCKqFIJoHEn/wXGKz3oBHMyHHl3dXj+D5Ub5O0ZqTOhZ4PLMggGtcXBIipFkoKp5dDG3HRFOBy3lQTMrnC0+xs5vbxGOFyWUA6m1d0gmOLBx+eiMzyCB5+WfiyPFw64GibO1MEZ/kluFVA/C4pw0HUgHHJjhuCCzAXy+KPjqyjWASEUDm+//TYeeugheXzgwAEsWrQIeXl5uOqqq7Bv3z6/5xUXF2P48OHIy8uT/61bty5UyyIIIkjUTa70HAPOrgQXnhJTYDMpaWzdcVXU+BEO9R3EIhScasT9nx3Ek+uP4l/bAls62h5OBniwhNuAjw2P4b6ahyE2lJ72WrtCnc0tm+Mj4ETyScWs3jL59xANUYFOhd3bgh21W+XxbFXfhFCQqLI41PDKOoJ1VQBAZmSWJrsiv85/YGH7QFebi8f+yrZeJyJmuDbJxzxpEzTBoYH4ofJ7WFxSfw89q8fCAVcHvW41Ca0C6mdRLRw6rg55/cCb5e0S6zHstuR3MLtv0G3hwPM83njjDTzzzDPyPpvNhmXLluHmm2/G7t27sWTJEtx7771+zz98+DBmz56NwsJC+d+CBQv8ziUIInxoCkBxLFh7nTy2iDFoMGTIY6659LRfR21xiIYdyWhAi5uH2+u/rfe7+Yp147+7Tvn9xdfk8KC03gE9vFhpeBJP6d/ERLYY57P7EP3ZdeAaS3zOCTWVKjfFVaYCsF4HAKm4kXPYNR2eu7l6E9yt1SKj9dGaHhGhQNOvwqPEqgTT6ErNJFWr7Z21/oVDe4vDwSorLC1uGOHGx4a/ILvic/mYJ73z9ymKIj4+/oE8npcxHwnGxC6tu402y8vPQra8T2cpAvjAwjU7OgeTkpU4jA+OvXtar30u0W3h8OCDD6KgoACLFy+W923YsAEDBw7ElVdeCYZhsHjxYrz88ssQBN8vhsOHD2PYsGE++wmCOLNoYxwYME4lYLFejEaNob9yvGbfaadktgmH0cwx/Gi8FzuMd+Nm7ls0tNgBj91nvqVF68Y42eDQjCuanHhzu2SJeFD3PiazhzXHzfZyxL8/D1E/PtLhA6K7qOM0FnGb5W3XoMsAzujvFJlvTn0pb89Mm9PtapHtiVelI9ZqLA7BuyoAbabHXstuuHhf65G9nXBoC3ydz+ZjIqstVx5MYOSe+t042qycd032ki6tWU1CpHQfjoiZ8IhSWiojuME1Hu3wvGtzrpe3Cyy75EqafZVufzofeOABpKSkYPny5Sgvl1J0Dh06hKysLPz2t7/Fjh07MGjQIPzlL38By/rqlKKiIrjdbsyZMwcMw+C6667D0qVLfeb5g+NYxMdHdD6xj8Fx0n2me+ML3ZvAsHolvz86Qq8pSWxBDKrilLbPrLMB8WIlkNC5fxoAnB4eRdVWjEyPQY3NhXRY8KbhOSQwUjDj4/r/AO/+B6I5HsKVb0AcpJjqre3qPuyvbcHYQUmAKODU/p/wwJpTKHIl4RruR9yh0zYucgOwsyyiBA/MB/4Lw+CZEEeenpm7s89OY+sDMxX1GMsrrln9hBs7/Lydsp7Cnvrd8vjaEdeE/PNpUMU41KuKQBm8jV16rfNjpsBcYIbD64BLcOG45xCmJk3T3BuupkVzTpvQG8Me0+wXpt+P6GHT0Rlr9iqxDVPTp2L8gNMv/xwVI8VFuKHHUTETwxlJcMa2FEPMHR/wvLlx52N86XgU1BQAAN488k/MzX0fLBPcb+9z7Xun2xaHlBTfXOTm5mZ88cUXuPLKK/HTTz9hzpw5+M1vfgOPx1ftx8XFYc6cOfjyyy/xxhtv4KOPPsKnn37a3WURBNFF1K6KKNjBCMr/V4sYgzpvBMSkIfI+pizwr64WlxcVjdIDY1uJBXOf34RFK7bjhrd2wukR8Df9W0hhGiECUNstGEcD2E9+AdT8LF+nrFFtYRDx894dKP/ySXhenoicz6/COua32G78Df6hfx07TUY8mpSA+f0H4LwBuRif0x/nD8hCXk5/3JKegvqSwGmE3aWiUXJVLOS2gG19V2LcAKDf5I5Ow+fH1sjbOTEDMSox9H0RIgwc2NZQggZVVgVa6vyfEAA9p8fE1EnyeHulb22DFrf/1tuj2OPyNj//7xDm/L9OX6+kqQSbypWYiJuG3dzB7M7RcyziIySrw34hR97PFH/V4XkMw+B3434nj4sairD6aN99ToWlyZVer8e4ceMwd+5cAMAdd9yBFStWoKSkBEOHDtXMfe655+Tt3Nxc3HjjjdiwYQOuvrrzXwU8L6Chwde02ddpU7V0b3yhexMYm0MRCka74qbwiiyaEInqRjucyeNgrpPMxu5jW2Hrv9DnOrU2F679Tz5sLh7Xjs3AR3sq5GO7TzYiFfWYze7FfoMBjyUloFbH4ZmaOkxxSmZvxm0DProVDdd/j4M1imi4mN2Bh3Wr0L+2FmhtedDAsvg6MgKHjAyKDGk4ZGz7ZS2CgdbFUWgy4X7LNjxlafLJWBBEEQ12j6aTZHs6+uw4PTwOlDUCEHG1yk1hz70K9kaHz/w23LwbnxxRHkALMi5BYwfzu0OkQQery4tyMUnZ2VSGBksz0AXXyKiYsdhU/iMAYFdlARoa7Jp7U+dn/SwEnMcowqE5aji8nfwf5AUvHt3xZ3mcHZWDYaYx3f6/G2fWo8HuwZfCFFwL6X0wR9ahseIURHPg2IkMdiDmpl+IDZVSKupLhS8hL3oK4o0Jnb5mT/3eSU6O7nySH8KSjpmdnQ2r1SqPRVGEIAg+QU1OpxNPP/20Zq7b7YbR2LE/kCCI0KO2OESLSg+DBkRBBItmpxfeNMWcq68u8Hudt3eckstKq0VDG5dz2/BtlBm3ZKSi2GhAA8fh4aR0lEeqKvo1HoNj/6dypsRsthCv6F9Gf1ZpkvRxdCQu6JeJJ5MS8Fl0lEo0BKZQL+KJ/Ifg8CoPN1EUsezDvbhoxXa88MOxDs72T6nFjotWbMf+SitGMCc0jcFcQzv+AbSh8js0uCWXkJ41YH7mxV1+/WCJNEiuqJOiYiVmBA9Ym+/fqCNGqepLFDcdlmsrtOHwUz58IFOBSEYShiLDwps00mdOe9499g4ONuyXx7cO/mXQBZ86IrHV4vCTMAothmQAACN4YSpe3em5y4bfjYjWQmhWjxX/PfLvbq+nNxIW4bBgwQIcP34c69atk7MuUlJSfKwNJpMJmzdvxquvvgqPx4Pi4mK89957uOKKK8KxLIIgOkAdHBnNK9H29aJUadDq9Erpc61wFv/legvLfBsnGeDBXHY37uC+xP8ZVuHxxHh4VQ+BOr2AR/rdBMcQ5UHr2PQ8Xth4BEOZk1iufwUcI63PzjD4Y/wA/DUpEW7W90EyLnECHhnzZ5iqfw/bkYdhLXoUU1sU8/lPdduwdPNt+Lbsa/Aij0PVNhSWS+9jVUE5bC7/pvZAfLSnAi2tD8uFnNIAypM6DnzcwIDn8SKP/6myBeZnXoQ4Y3yXXrsrRBol4eCCAQ5jsryfa+pat8tB0YNg5qSHJy/yONSorYPQPjgSAMYwSlYLnzAE0Hfs6z9Qvw8rj7wtjy/MvAiz0ud2aZ2BaEvJFMBiX7ySwWc69GGnAb9JpmRNA6zvK76Dm++4Bsm5SFiEQ3p6Ot5880288cYbmDBhAjZu3IhXX31VVot5eXnIz5dyYZcvX46ioiJMnjwZd955J5YtW4ZZs2aFY1kEQXSAugBUNK88/C2twqHZ5QUfPwiCUap+yEAM0F1QO+bA4xPDn/Fvw7P4o/49vBsbjWaO8zkv3/oxbizNk8dD2TLczn2NtwzPIppxwM4weCc+EQtyR2BNnPIFH6mLwsIBi3DH0GV4eerreHbyy7ggcwEGxw6G6I0FhAjMrxqA6XbFylBmP4Wn9j2OX2+5AztqtwGMW/oHoOBU5x0j1ew40SayRCxglRx/55CrAp7jFbx4eu8TKLEqFo5F2dd16XW7SpRBcUc0m1TFvLooHDhWh5Hx58njA/XaGj3t0zEBYBSrCAdP8hif42psHhv+tvcxCJA+j+kRGfjtiPu7tMaOSFC5o36KUIpa6SyHoKve7e8UDZf2uxxGVrKK27xW7Krrez0sQhbjcM8992jG48ePxyeffOJ3bmFhobydnZ2Nt99+2+88giDOHOoCUJG8UoWxHq3CwekBGBaerGkwHpOCyfSnNsGdo3z5CqLYLpgRGMMcwyi2VLoGy2BljNIrYZBpBo46toJhBLCGRhQl/w/vVI7DrYL0Bf5H/Xv42aDHy7GJ2BhhhpNlAa/i2syK6IdnJr+INHM62jMwMRJbj0sP9UJ+OF6p3oaX4+Pwn9gYiK3i5khzEY40P4bo1oxwwRuJ14+OQErSUgyPG9HpPatqdspZA7lMObLZavmYe6D/ejRHmorwzL6/45iqpPfMtDnIjs7xOz9UtFkcAKDemIVUSN/Dp1OTY1T8GLlL5b4Gbctpf51OR7LKa3hTOg7+fOngs6h2SG3JWYbD/xvzGCL1kV1eYyASIpT4liI+He6MKTBUSA9/88F3YU0LnF0BAGZdBKalzsDGSql52fcV32F66syQra83QCWnCYIAoI1xiPQqwsHSmr5XarHjcLUV7izlS9JQ9pPmGlXNLjg82not6k6Iq6OiYG1NTYvgInBJylK4axXhwRoa8FxmC5ampuLqzDRMHZCFJZnp+DoqUhINrZg4E24YdAv+Of0tv6IBAAYmKubwrcJI6ADc19CINeUVuCB5ht9zWF0LqoRd+M3WO/De0Xf8zlGjWBuAxZHKL29P8igIURk+84ubinDv9t9oRENe4ng8OPqPnb5Wd4lUWRxqdaoqoF20OADAqATFavBzw0HwgiIWfC0OIgYzSjdNPjFw3Z7vy7/F9xVKP4hbB9+OESrrRihQl9+ub3HDeZ6SqcEWrQFv77y2xdyM+fL2turNsHtbOph97kHCgSAIANoYhwiv8kD0tEaN8yLw56+LUKsqAqRrOKoJriux+H6BtgkHAcCK6DR5/8IBi5ARlQC3ZQ6cVZfJ+0WdA1sjjDhiMKClXe2XSF0Urs6+Fu/O/h/uGLoMUfrAZZwHJSm/Uo+LaTghSEGBOR4v/ho5Fi9NeQ3TU2eCga/bBADeKn4dm6t+DHh9ANhxQhFYC3SKmdudM99nbqW9Ag/vug8OXomsv6Tf5fj7hGdh1pl95oeaKJXFoYrrnnAYGjscbOvjw8HbUWotlY/Z3VrhmIRmxDHK58IbP9jvNa2eZrxy6EV5PCp+DG4YdEuX19YZbUWgAMBi9+Bk0hzZHWcQ3Ti56T+dXmNi0mRE6SRB7RJcyK/dGfJ19mRIOBAEAUDrqjB7lAfi1BHKF32JxY5rV9ehJaKfvM9wUsmzL6nTppsZ4cZ4Vvp1vdlsgs3QGlkvMrhiwFWIazUbexpmwFG+BKLo/ytpSMwwPJr3OD6ZtxZ3j7gXCUGkwA1LjUJeZptbhMFGQckGMJzYgFEJY/D4+KcwR/8abEcege3Iw3CU3QjBo7hSntjzZ3xx8jO/Za5LLXb8eFSqgxCPZgxwKu2ZXdla4WDz2PBI/h/Q4JYEGctweHz80/j9qIdh7KSqZKhQWxwqGEXAcU2lXa4CataZNa6VgxbFqtTe4pDLKtYGwRQfMOXx30VvoMktfe5MnBkPj30UHONf1HWHeLXFwe7GE9+fwIf8bHlfVmnnQZIGzoApKUrVyy01P3Uw+9yDhANBEAC0wZFmj2KuzcrIwk0TlGC6uhY3PrMqhaAiCpYDbukX5bF2FofxbDGMjFQfYmVMrLw/XhyLVHOaxt/sbR4Le+ldcNXNhbPqMixKexCvTnsTn13wDVbM+Ddmp8+Dges85bINlmGw4rox+PWMbADAD4JiXtef2gy0RsPbnCxEbwxEbyy81lFwnLwDOkgWALfgxgsHnsGjux9Cg6ohlFcQ8dg3RXC3WmnmRxTLHR/5iFTwSUp8BC948cSeR3HCptQxuP+8BzE99fyg30soaEvHBIAT6pRMrx2Mo2uFoADJ6tDGwboD8nb7rIpctZsigLXhpK0UX5z8TB7fnHtbQBdUd0lUfeZsLh7bTzRgFT8PQmvgSwZfDs/xTYFOl5mm+vvtqNkKXuhaNk5vhoQDQRAAAK/KVWF0KxYH0ZyI387Mwf1zBkHXmv64yjsP3lbrANd8AlFbnwAAHLdoLQ4XsVJ1yVKdDtsjlF/Wvxp5IwCpGI8awdkP7tr58DTMwHVDLsLwuBGIMcTgdGEZBjkJUqzDdmEE3JBej/XYoK+SMiAa2nXmFNwpGI67EWuIk/dtqf4J1311LXZUSVkkW0rqcbBKCdL8VaZSu8GTNU1OLRFFEa8dXo6dtUrk/Y2DbsHF/RTXzJkiyqiKceAjIRgVIXe67oo2DtarLA7tgiMHMYoryxughfaqYyvlLIqsiH64Juf0+1F0RnKUUSOiAKBMTNYIS2f+fzq9zsSkKdAx0j1t9jTjQOP+Ts44dyDhQBAEAMAjN6ETYXQrFgfBnASGYbBkXCbeun4sMmNNOChmYwV/uTzHfHAlTD+v0jSkevOydNxkkGIEPohRKtT1i8jGgmzJzKvnWEzPkdwOGbEmZMRI4mLygDgkRYXGhB/bKk6cMCKfUQoPGU5sAAA0OnxL4bc05eDNGf/FhCSlvHKtoxZ3fb8M35R9iW2lyv2ZOSgRA6xKMSx3lhR4KYoiXj/8Kj4tVXotzEybjV8M+VVI3ldXUT8sW9w8+NhseXw6mRXD4hThUNxQDE9rAzEfV0UnFodKewXWqwIibxp8m09lz1DCsQxGpvlWTFzFK/1Rsmp/BOO2+sxRE6mPxNjEcfJ4a/XmDmafW5BwIAgCgBIcGQM7WFExuwoqn/SItGi8e/M4zM5NxMveq3FA1Z446sdHkOFUsgVGnFoJVnDDzjBYE60EMS7KWaypAPiPhSPw6jWjsPKmPKy8eRxeuWYUnl3YeWXBYIkxKb+0N3iVX5WGExsB+BcOZU1OJJqS8NTE53HXsHvkX5YiRDy770lsqv0MgPSAvLifFzpV225PptS4acXh5fjo+Cp5/5CYYXhozKNBN0YKNZEqi4PN5QUfM0Aen47FISd6kPyA9wgeFDdKpcjVwuGBebmYHKVU+/Qm+AqHj49/AEGUzkk3Z2Be+oU+c0LN6AxfK5Z+8AWoF6XPqQFuGEq+6fQ6U1OU7Jz8AG3Gz0VIOBAEAUCJcYhr7VgJSB0fRJVJG5BM3r+dORBu6HGn+37UiK0FoQQvboD0ZXsFuxXpRVJ9lo0RZthaXRyRukhcmKmtb6DnWEwaEI8Ykx4xJj0mD4iHSR+6oLhYlXBY71U6fOrqi4DmcjRphIOI2WwhrnZ+CldLI1iGxeKB1+OVaf9CokkSUAIEOGNXI3LQszAkf42h4jb5bD5mAISYLKw9uQYfq6pC5sYMxpMTn4WJM4XsfXWVKB+LQ/eEg57VY1C0IgT21UnpqOoYh7xkFgaHUtuivcXB4bXj23KlwdS1A28AF+KW4v4YnekrHGbkpuIrXmlIFkwJ6onJyvzjthLUOms7mH3uQMKBIAgAUsAfAMRD1WfGGAuwvg/xrDjJrVCJRDzhuUnefym3HXPYQjyv/6ccLPhFrJIBMSttLsy6M9taONqkmL1LxXS4opUHpliyHm2hHWY48bz+NfzH8A/8P/0qxHz5C0CUxNSQ2KF4fd7riFeVhGYNDTAm/Yjbq/+LB5MTsclswrr0Ybhvx914/sDT8rzcmCF4fvKrQTVDCidqi0OLy6t1VTSVntY11fUcdlXthCiKmhiH+Jaj8raoi4AQpQ14XF/xLexeKS4mUhcZ1l4das5L8xUOA5Mi8RmvtPnWl20G28l9yYzIQrpZqdfRV6wOJBwIggAAuLzSQzJeZXEQTP57JzAMg0kDpGPrhAloFiUxEMm48LbhH9Ax0rVqIxKxXfXAuiDTfzXFcGLUsTDplK+6faaJ8nZEsdSZUgcv3jC8oOlsGVO7CzHr7gJrlYL7BsXl4oNLPkSyMAOiqLhaeAb4KioSv0lLwYPuQ9hjUeo5xBvi8fj4pzqsN3GmUMc42Nw8+Jj+8phr7rrFAZCKV7WRX5MPh9cLVYwt4lVNqjwpowCVm0YURaw5oeoMmnXJGalnAQDRJq1VIznKgDizHgXiELneByMKMGz+G74vrm1nlVJgGAYTVFaHXXUkHAiC6CN4BVEWDnFQuSoCCAcAmNwqHFwwYC0/xee4yBqwevyt4Fuj5ZNNKRit6qx4JlHHOfy9TCl5HFVbgEnMIfxJtxLns75R8cZjXyJh5RQYWktsp0SkgKu/Di1HH4Kz+lIkiYEDOEcnjMWzk5cj1ZwWcM6ZRJ1VwQsiHJGKcGAdFqmdeQecbHDgYKW2qdmYhLFyrQWr24qfLYc1xyMblGqa3hTt335H7TaUWBWLxBX9A/f2CAc3q1KM/2/mQMSZdBDB4nnvNfL+6NKv8fmXH+Pe1Qf81vIAgEkq4VBQtxPePpCWScKBIAg4VX7peEZxVQSyOADAxP5xMOulrxB1AZ02bNP/hO9tyoNkXsaFZy0wMFaV9lkoDsY2Xqmz8JLhVdyq+04eb+ZHykFygPTLM2L3P+WxpcUN0RsLtn4y1lZY8Hx1Lea22BHPmsAyHMYk5OG5ycvxwuRXkRMduDvmmSaqXQriDosBoqr4FNtBnMP+imbc8N8C3LZqD1YVlMn7zboIDI9TAlkLLPma80y1e+VtT6rSwEwURaw8qvQompIyHf2jsoN/MyHg9in9cceU/rh/ziBcOCwZOo5FtFGHz4Vp2CMof7cV+hfAVO1Fjc1/F8y8xPFykKjVY0Vhu3twLkLCgSAITWMiTXBkB8Ih1qzH01dID+C9Yi7udy/Dd/x4rOfzsDJmGU7kXoSfG5XCQHPSLwjDyoMjtp1p+hV+obydziiplRZTNn7p+QNudj+MMp3yi1xfswdoPAleEOUsjKu4zYh01+NCuwMv1tvw6exPsO6iH/DClFeRlzhekznSEzDqWHCqJf3us0OoUfes6CAl8+VNJbJF6oUfSjSfF7W7Yl+DkpaapmuBrvmkPPamKPEQO2q3atpx35z7i669mRAQZdRh6fRsLBmXCbb1bxVnlqwOf/bcBg8jFRuLYRxYYXgBx2sb/V4nQheJSclKFckNFevDv/izDAkHgiA0KXSJQcQ4tDE1OwEPXyAV9flEmIk7PffjDs8fsDN5MX6o2ijPy4roh9yYIYEuE3Zi/AoJmwAAIABJREFUTNq6AFuE81Ci8y1GdKr/1XDBgINiDm42vKQpWOTe9ylONdghiAADAXdwSjaAc+hiiKb4sJRIDhUMw2jcFQCwz66k2gbKrDha24I95VoXxRcHquTtcYkTlLnWgwAjmeon6JVKmYI5EUK05Bpo8bTgxQPPyscmJE0KqhPpmaCtINleMRfPRD0Ejyj9PTMZC5ij6wKeNy9DSSHdXP0j3LwrvAs9y5BwCAHbS+vx2ubjqGx2nu2lEMRpoRYOSZxSNroji0MbaTG+KYbxEXr8UKn88pqdMe+s/gKPMbVP8WPwtte3EZVjoBLVX9vihj3nUnl87IeVuOAFqSfBpewO5LJS0KQIBo6xd4Z+0WGgfcVEdenpQMLh/d1lfvaVQ2j1+Q+PGymnmXpEFzizZGUYyx6T53tSxsrVNN8o+idqnFKKpo7RYemwu0/37YQctUtrZcMIfCko8QuDyqRAToeH94l3mJw8DSZOCuxs8bZgh6pS6LkICYduUt7kwO9WH8S/d5zCX74pOtvLIYjTQp17n9AFiwMAZPgRDqzBgqImJb5hTvo8nzlnkvYWBwD4yDlJM27RxSMmRfFt21w8bspXmnmNYo9jKnsQUbDjT/qV8n53znzwcT0nlqEjrC5tVcdSUQnc1NUdbD8dALD1eIPPvvImJ8obpR9KBs6AsclK4CMXIQU8TheV7BJvmlRhsdBSgC9OKvURbsy9FYNi/JehPhvEq4SD0yvgA36uPB7qKMD3O/Mx95WtuHHlbtl1A0hNv6apikFtrDy33RUkHLrJVwdr5Pz3glNNmiAzgugtOFStkINJx1STFuObWVDJK22GB0TlICd6UDdX2D2ijb4uBBcMeMl7tTxunHA/EiINYFWGkX3uDOwUhsrjP+rexW91q5HKSP5ukTPCNu2P4Vt4iLG6tBH/uwWlIJOudr9PZkWL24u6Fv9BgdVWxRw/KU35Za6LPIZ0WDBcUKqIurIvhMPrwLP7n5T3DYzODUvb7O7QvnfKdmE4SgRJXLEQ0bjtTaSJNaiorcP3xdpiT3NV7opt1Zvh8Gr7tpxLkHDoJuXt3BNHalsCzCSInova4hCrLgAVhHAw6TlNl0sAOObcKm+fbWsD4PtLu43l3ivx76ilqJvxdxjG3QIdyyAhQtuB8++eG+XtkewJ/Er3pTy2j/sNhLgc9BbOH6gtQnVI7A+3XiqGxIg8dJW7NMdPNjjkbaOOlXs85DFHYDi+DlzDMUAUMSlNsd6w5lOYo1NM9XzMAPCJw/FW8euotEvuHZbh8MDoR8Lak+J0aC8cAAbv8UpQ7zLdWmw2/h/yjXdh8J7HwTiVgMkJSZMQpZPuj0twndO9K0g4dJNDVdpGKIeqO86FJoieiLraX4wYXDqmGnWcA2OoRbVLCYyb3QOEw8BE/9UqvdBh6qIHII65RS5OlBylFQ57xFx8xk/zOdfORsI+dmnoFxtGbpyQpTHHi2BRHq24GQwV2zTzT9YrwqF/vBlpMUZcz32P1cY/Y96B+5GwahaiN9yPYfHDEG2QHpoMI0BMVISDa9DF2G0pwGpVs68lA2/EkNhhIX9/3cVXOEipxs2itjCVmXFjWsNniNr8Z3mfgTNgZtpseby+InAwZW+HhEM3sLm8Pm2Ei2o67qhGED2RNouDAR6YRMWKJhrjAp2iITVacVfoY5Tc/UHRg9E/aoC/U84oFw5NxrCUKBh1LO6ano2UKAOMOhbPXTnSpwtnsp+unM94lsApah8qexMuBQyRYV13qBnfLw7f3DUFFw9XgiKLTYpwaCr6UTNfbXEYEG9GrsmKR3SrNHNMhz+C7uh3WDjwSnnfung3vo8wgwfwUXQ0Htp1H8TWEuQDorJxy1lIvwyGuAhf4WBDhCbWQY3x2FeAR3kGqN0VO2q3odR63N9pvR4SDt3gYJUV7WuJHSaLA9ELacuqiIX28xusxUFdXEgXo1QL7AluCgAw6Fj896Y8rP/1VNw+pT8+u2MSNt49DTMHJfrMbW9xAIBXbr8Y7+Ayzb4jWdeGbb3hhGUYjTjKh9IeO9l2CA0WpSnViQblodg/3oyrLK8jmlHEhHzNdQ9i6bCbYYD0efEwDO5NTcbs/ll47tSH8LZ2W9UxOvxh1CMwcKFpmR5q/FkcAODf3ovkomBHhEx5P+N1yO3ZAWBs4jhkRymuq/dLlCDacwkSDt3gQLvyqwBwzGKHWxVtSxC9gbaCPurASJEzAvrgegeYW7tZssYqcMYaef/sjJ4hHACpjkFb1009x0LP+f/6ay8c9ByDjFgT1sUtkduI/8c7H1xi78ik8If6PX5YHoc6UYpz0DECUPCmfExtcRgaYcdQi1Jh8xP95RBbO1kyTacQfehzpDlvAKPq49GouscJxkT8Y/JLGBF/XujfUIjwJxxGpccgJSMHl7v+htvcf8DF7ifxCa9kUJiOfiFvswyrCfj8vuI7Oa7jXIKEQzcoqfONmuUFEccsFCBJ9C7aLA7x6FpGRRuZcVKMg9raMDR2GDIiMgOd0mNp76pIjzGBYxkkJyZhkfsxTHe+hL94b0FipK9loreQohIOzS4R76hqWgw4/i4YtxWiKGqEw1jHVrnjaZmYhL96boTjvFvl4+yed+G2DcONZZm4oMUOTlXrYFT8GLw+/W2MSVDKTvdE4v0Ih4xYI/526TDw0Vn4QciDFzp8qerNYjjxvcZdMSd9ntwxUxB5fFjyXvgXfoYh4dAN3Lx/ywK5K4jeRptwiGO6llHRxsJRaYgzc9DH7JH3zT6LJaa7Q3uLQ1arKMqKN8MFA8qRDBGsT/ZFb6K9OHqHnw9rawCg0WuF6dCHsNg9aFEFzfarVUzy6/iJaHIKaBx6vbyPqdqH5JYizHfX4IWaOnx7qgL/Fz8d9533AJ6bvByJpqQwv6vuE2XkwLHaQmXTchKQFmPC+7eOxwe3jsfw1Cj8JIyWAyYZrxP6CqUrJsfqsGSQ0mr+67IvUevQpm72dkg4dANe8N8traiGhAPRu7C7g2+p7Y9Igw4PXyGANUh9H1iwmNtrhYP2odpW4Cq23a/R9imovYn24qgZUXiPV9xKhpKvcVIV39DP5IK5Usm4+IaXWpM/so1Hc6ISXHmx5zsMZU4BAFJ4Hotyrsdl/a+Ejm1fubNnwjAMpmZLn3ujjsX/u3CwHEgaZdRhUFIkchIj4IEO2wWlTLahTJt6uSDzYiQaJaHkEdxYdfjcsjqQcOgGvMoUNzhZia6mlEyit+HPVdEViwMArK9cI29PSz0fyeaUDmb3XJLbuSCiW8tVJ7Tb71vGuveQ5MfN8jWv1GLQV+ajsb5OHl8ZsRdMa7voesSiQJT6jvxw1IInq5Xzbv7/7F1neBzV2T0zs31Xu+rNkixLtiVXLNvYxpgeijGm1xAIEHoLAT4gCSkE+EgICT3UhBI+MB0MOKHaYMA2brg3WbJ6b6vtO+X7MTtz72xRlyzZc57Hj3dm7+zcLZp75n3Pe17uc9gY2RhKYljwaUR4OVbw0BlT8Nczp+K9q47E2TNzYqzSx6fIpb3fiUSrYaz7XjPGxJlxwYSL1e0P9n+IsBAexlmPLHTiMAiIVKZiasQYBQDKWzzgE6QxdOgYjVDKMV0M0eeIfSzFBIAD3ZVY10LuSM8af24Po0c3oglBUqQx1OnTs9XnjixIHnXdL/sDA8fGREy2SkVokYgZlKNhtfrcKQJ5vMZ0FERq6VjOz4dXiq2SEFxFfRbXjiZYjByOn5SOzKT4lR+KJ8j3ImknbmjZDiagteZenH8GTKxM0DqDHVhVt2p4JnwQoBOHQYCnIg6TMxxqbiwkSKhsP3TtRnUcelAMoBwgv1vJnJRouAaiJOLR7Q+rdfr59gJNq+WxBoZhMH+8TJrMBhaLp2YBAJxWI16/ej7uPKEY958++syL+otZ41yabQksvhZJ2mFci0wW0tGFacHN6v71dq2ngRdWjVhQAZ89uoWQA8WMXJlc7ZPGEaIFCcY6rXlWktGJY7NPULffL39v5CY5zNCJwyAgUhoHi5FFMeVOpwskdYwlKBEHukZfMvWNOHxauwLbOojp0/Wlt4Blxval5XenluCGowvx5HkzNGH90uwkXDR73JiuqFBw10kTcd3C8TjviBxcvaAAAPCVQIjDpO7v4YQXZ3BrwEKOoAqOXDgnHq2OmZIlexu8JRyneW0BLHxlNw73WzgoSLObMM5lAcBgDRV1oAWSCpbkn6k+XtuwFnXeWkiShC11XajpiPXDGCsYu0m6UQBaHGlgGZRmObA30qtiT7MHSw/WxHTEwB0I4/WNdcjPcODSefm9H3CYQdE4OEAuZmIfiIMkSVhW8Zq6fWz28Tgq6+gejhgbyEoy46rIYnqoIs1uwtVHEVfPt3+sxzeBmfBKZtiZIOyiB9+bb9GkJYKTz8YFZXkIiww4lsGlc8bhure2YkN9CXaL+ShlZWHkC8m347zUSTHnPFQwI9eJuq4AtohFOJOTIw2GzvKYcTNTZyHfXoAabzUkSHj3wFvICl6ER1buB8cyeO1nszExY2y5jwJ6xGFQoMWRHMOgJJNcaPWIw+jCKz/U4p9rq/HHj3biy92HVmnUUEAxgHL0M+KwuW0jarzVAAAGDK4pOTTvMg8HZDst8MCGZ3lyy+NgAnAycvpKYo0IlF4Ii5HDVQsK8PN5+TBwLC6dMw4Ag+vDt+E1/iTcFroRu9JPP0jvYmQwM5KuqJRy1H1c54GYcQzD4LzCi9Tt/9Z+jEe+3gZAvvH838/3Du9Ehwk6cRgE6IgDG4k4KNjb4klYrqlj5PHq+hr18a/e2tLDyMMPkiSpEQcnrXEwORIdouLDKpK3PTJjAcbZ84Z+gjpGBDmR9ugvCEvQbczQPMcb7HCf8hSElIkxxx1TnAaX1YgDUg7u5X+BD8RFSLWP3VLVvmBmjkwcDkjZ6j62uwYQYluQn5K3GMkRoXFACMCcQdw3tzWMzd5GOnEYBGhiwLEMJmXYoXiH+MPimM5hHcrwh+O3WD5cEeRFKD9lOlUhmZw9Htfib8Z3zURtf/YYrqTQAYxzyRUQAZjxpPNONEvJaJBS8Tp/AvafsRyh4iVxjzNyLE6eoi29TRnD5lh9QXGGHWYDixopE7wkL6OMJIJzV8eMtXAWXDSZRB1MqWtgcG1Ut8diBZ5OHAaB6FSF1chhfCoRSO7SO2XqGAMIhMmFS5uq6Dni8EnNcoiSTMKyrTk4MiNWWa9j7KAsj1RZPF+Xj3nBf+Co4FP4DX8NkrJ71iucPiNbs20zHtpLi4FlkG43IQwDaiUSneE643fDvGLqlShNIZU4lpx3VfKg6OLGEg7tb3eYQfs4KL1cplDpisNV57CpthPn/2s97vxgx6hl0+FROq+DAZ8agZGQpElVJNY48CKPj2uI4dPSgrPBMVzC8TpGP+bku8DFsaZwWQwJG4IpOKpI22W0KG3sCf76C6XappJKV3CdFXHHWgwW/O3YvyHJECnfZERYc9+GIWkrfqzrGv7JDjF04jAIaCIOkRxFSSYhDoer9fR1b25FVYcfX+9vw392Nfd+wAgguutdbWfgIM1k9EEhDhaE5O6IEfRUVbG6cRXag20AACNrxOK8MxKO1TE24DAbMD0nNj3Vl9JTI8fi8YuOQIbDhFNLMzA9p2+lvGMZ6RHbblrnwHXFjzgAQK5jHG6d/BeIPFkjLLlv4/E13+LXH+2EKI0dTZxOHAYBjTgy4iJXGhVxGEs/huHAuqqO3geNAKIjDFW6QZcKxfyJjjYAiVMVgiTg1fKX1O3jsk9Asrl/9tQ6Rifmj4/9HuPZU8fDkhk5WHHdAjywZMqYdtXsK9IiOo4KTWVF/IiDAhvy4au6DpIg9z9h2DCs+S/hy8o92Fw7diIPOnEYBKJ9HADZQVKBNySgvuvwurMNRAkPR4NISpKkmHlV6cJVFXHNn1gjwFnijl9Z/wWqPPKdFQMGFxddNvyT1DEimDc+1mY83XHw/4ZHI/obcQCAdm8YUigD/rqfQpLkNYM1dsNW8E/sbKsavskOMXTiMAhEiyMBOdxXkEL82Q83nUOjO6jZdpoPvsdYSJAgRAV+9IgDAbGbjhJGxrlrFCVRE204IecnKHIWD/8kdYwIpuc41V4MClyWQ7u0cqBQIg4aLwdPAxBKLHZs98nlmoJ3MgqFy9X9rKkd79XdhuquPcM026GFThwGgWgfBwW0zmH3GNA5SJKEzbVDY4Fa59ZGWEKjQIQYr/xSjzgQkIgDLYyMX4q5sXU9ainDp59P+sXwT1DHiIFjGfz+tBLNvmxn/GZPhzvSIhGHOikNAZCoTDwHSQVtXtIhc7L1REw1/lTd7mKC+P1318ETHv3VeDpxGASifRwUlNLEoWn0/wheXV+La9/cgvP+tR5//bI8JqzfHzREpWZGg2dCvDk0uA+vFFJP8MaJOIgJ9A0fVX+gPp6fuRD5jkPblvlwxLTsJNy0qBAAYDGwWBRVMaFDhqL9kMBivzRO3c+170t4jBJxAIBUmxELks/CDc0CmEj0uhohPLzudkijXBt38OPIYxi0MSRdxhQtkJQkaVSLhVbvl9XxEoC3fqxHgBfwu1NLej4oAaI1HbRHwMFCPOLQ7gtDlCRV1Ho4wxvkAUC1FgbiCyNbAi34vvlbdfvMgnOGf3I6DgqumF+ARcVpcJoNCdtLH+6gq032iOMwjZP1DYaOvajsCuCNTXWYnp2EUylzLA1xsJswIbALi711YIxO/CNF1pd8696BVQ1f4ITck0fonfQfesRhEEgUcaBTFV0BHk3d2rz/SGBngxv/Xl+DVm+sBWo0uiMLh4Itde4Bnzf6Tn5URBxCsXMQRAnuAB9n9OEHT1yNQ2yq4rPaFarhU5Y1G0dmzB+ZCeo4KJiYbtdJQw9IsRpVp+ByURtxeOiLfVi2qQ73rtiNjTWd6nPtPpKqSLMZMbPpHQDAdZ1uLPSRv79ndjwKHz96jaGGjDi89NJLuOeee9Tt7du347zzzkNZWRnOOeccbN26Ne5xgUAAd9xxB+bOnYvjjjsO77///lBNadgRz8cBAFxWI3KpvOBICyS7AzyueHkDnvimEj/796ZeyYMnijgMZkGti4448KMh4hB/DjT7P5yhRBxixJEUJEnCp7Ur1O3T85bqhk86DmtwLIPUiEBynyZVsRdrD5Ay9D99ShpZtVHX4jQLkN34FQB5If5tWweMkTWlNdyJN/aTrrOjDYMmDoIg4Pnnn8fDDz+s7vN4PLj++utx2WWXYdOmTbj44otx2223xT3+73//O/x+P1avXo2nnnoKf/7zn7F79+7BTmtEEM/HQUFJFtUpc4QFktvqutAe+YG2eUO495NdPTbc8gS1d+TuQHjAObaGqKqK0RBx8CWYQzslVDqcoWgcNOWYURGHHZ3bUesjjcJOzjttZCanQ8cohpKu0BAHdzXMIAShviuA8ma56WGnn1xzCrxbwUWiCn7JhJWBE3BlJ4n2vnvgTbQH24f7LQwIgyYOd999NzZu3IgLLrhA3ffVV1+hqKgIZ599NhiGwQUXXIAnnngCohh75/fxxx/jhhtugNVqxYwZM3DGGWdg+fLlg53WiCCej4OC0oPoILkvqkfGxpquhEZMvCjFLKyCFEsm+gJfSND8YQCxvg4Klm9rxK8/2oVdIyAeTTQHPeIgw6NGHBJrHD6jog1laXOQbc2BDh2HOxSBZI2UCZ6RHzOQUMzUa8a9tq4anb6QRheX1fyN+vh7cRpWibNwVZcbqYJ8vQoIAbxW/vLwvoEBYtDiyLvuuguZmZl48sknUVdXBwDYtWsX8vLycOutt2LdunUoLi7GfffdB5bV8pSuri60tbWhqKhI3TdhwgR8++236As4jkVKiq33gcMAUZRA35OnJtuQQvk3zC1OA747AEBuYjKS8yyP0zSlLSTEnUNnosXTbOj3nFvikICQKMW8Tn2nHw98vheSBGys7cTae07UpHqGHMb4IXW/BKRYgmD2fQap4Cgg+fCsEAhGrmZ0xMHsSoUp8r2FhTC+bvpKfe6cyWcftL+7gwku0q/hcHzvveFw/WxyU21AZTsEcGizFCDLL5diTmJqsVMqVMftbuxGF5UCNnIMnPWr1O2V4ixsEEtgkxhc3enGw2myg+d/aj/C7fNuRVIvnWpHGoOOOGRmZsbsc7vd+Oijj3D22Wdj9erVOOGEE3DTTTchHNbejfr98oXKaiULrsViQSDQ91K59zbV4ex/fI9l62t6HzyEEKJC+WzUwjctl3zRzd1BNHePXPnf3jiaikR33d0J9Aydvv6H8ZvcsSJQXxxh4v4WL5SPr8MXxrflrf0+V38QTxwJAGjeBe75ReCW3wDulcVAaPR7bgwHFHFsEqVxgJn8ftc2rkF3SCaFZs6ME/NPHNH56dAxWkFXVtSaJqiP57PadHtLdxBdVDR2gaUGTPt+dfsroQzdsKHVWYoLu7uREok6BIUgPq/+YrimP2AMSzmm0WjE7NmzceKJ8gXm6quvxrPPPouKigqUlJAyP4tFtrQNBAKw2Wwxj3uD2xfCXe9tAwDc++EOLCpwwW4amQrT6IXY0x1AB5WK4QBkOExo8ch39Ov2toxIPbQkSdgb586/0x1ER0esW2JdgjRKbUs38u39c4yrinNeb5CPOW9bp3b79bVVmJkxfN302uLYfmeiA5fsvAeMKM+Z6W6Ad9unCBUtHrZ5jFa4IySRbqnt5U0IRr635Xs/UffPz1iIkIdBCIef86ZyNx3v7+hwx+H62RipG8jN3CzMwecAgGO5rQAvAZBvKJu7g6ruzA4/HhQfU4+rNRWjPpAOACg3z8BR7p1Y7PHhdZesk/tg74c4IW14NEUZGQNrRjYs5ZiFhYXo7iaLiCRJEEUxRnCXnJyM1NRUHDhwQN1XWVmJCRMmoC+o69S6/yW6ex4ORGsN47Wj1RpBjczdbLMnFPdzCPAJIg7B+J9Zl7//n2W86o14Pg7eqAjA1+Vtw6o3iCfQvJhbCbuoJTqmqlXDNofRDG+cJldKS+2gEMT3zavV/SfknDSyk9OhYxTDYSE3quuYI9THeUyrRufgDwuoj6xXvzG8jgJJfk4Cg5V5N6njdjOyffuZHpJu3taxBXXe2uF5AwPEsBCHU089FZWVlfj000/VqovMzExNtEHBkiVL8MQTT8Dj8WD79u34+OOPccYZfWvR2+bRLjbR6YPhRHSVQrwcPW0ENVICyYq2+LW/iYyYoksxFbgD/U9VtMUjDrwQQxijiQMvSrj/073D1kmUJg5KY5rjuC0x40zVq4BR7tg21JAkiYgjNVUVMnFY2fAFfLxMKCycFfMzF478JHXoGKVwmIh+qpZ3wpcyVd0+npWvMWaEcCy7BbUNDZjM1OBijuiFfHNugTd3kbq9KZQPAJgaCqE4RK7Bn9f9d9jew0AwLMQhJycHL774Ip5//nnMnTsXK1euxNNPP626J5aVlWHDhg0AgNtvvx0pKSk46aSTcPPNN+PXv/41pkyZ0qfzNEaZDYX50UUcSjKpkswRijhUtMYPFQYT+CkMZcQhHnEQJbnJFI14uodvK9rx3paGfp+zL6CJQ36KDS54MIuJ9ZPnPHXgOhLbxR6KCAkS+Mhv2RkVcZAkCe9UvqnuOzH3J7Ak6JipQ8fhiCQq4uAJ8qhOXqBuLzVtxDg78Kbpfrxq+gvu2Hk+/m16CBwj/73xyUXwHfkrFKaS1Py3nSmQDFYwAM70kDXj87r/jiob6iETBNxyyy2a7Tlz5uDdd9+NO3bz5s3qY5vNhoceemhA54whDnHKPYcL0dENLo51MR1xaOwOotMXRrJteDvN1XTGb96UKFXhSSAc7BpAxCGR0ZQ/LMBsIBzVG4pPSj7b04LzZ+X2+7y9wRciv4uCVBvG1W1T/3gFawZEeyaMrTsAAKaqr+BPnTzkcwAwKq3Hle/CDr+mqkKwZWJz20ZUdBOCdV7hRSM+Px06RjOSqO6/3UEBG01HohT/AgDMknbhO+Gn6u25TfLBRtm6exfeC3BGTTfSjoAIf/4U2Fo2YYnHh8dTUyFCQoO/Hts6tmBm6qyReWO9YExbTke3cI6+sx1O9CXikOkwIcVKiMJwpCskScLWereqa6AjBcnUuRNFHDwJdCFdA9CLxIs4ALFCUjrikEM5bCZKmwwWNGnKT7XiOJakKXx5xyJU+BN127LnvSE/f1W7Dze+vRWLn1uHr4e5gqS/UPw6shliNCOxBki2dHxSQ/xU5qbPw4Skopjjdeg4nOEwaSMO3wSK8bUws9fj9riOQahQ7kWRYtOuEy32SQCALEHAkaxL3f9Z7X+GatqDxtgmDl3RqYqRizhE5+PjEQeGYVBCRR2Gw+zo8a8r8Ys3fsS5/1oPf1hAd5BECjIcpFQokcYhOlWRgzb8yvAO8jt/6PdcoiMOU5kDuMfwOvK/uBLWLS+q+gFa45BOlTPFS2EMBejXzXVasIjbrm5XuRbgM/YYddvQthOGlu0YKmys6cT5L23A+upOtHlD+Ofa6iF77aGAEnHIYiiDMEcWwpKAH1rWqLuW6g2tdOiIAS2O5EUJu5q9uDt8DdxS/MpAUWLwLL8Un5f+GaCij0XpZHw5W6w+XkqJxr9t+hqCdPCdeIEx3h2z6SCmKvioiEOiLoulmQ7Vt3w4Ig7/t1FW23b6w1i2qU7TZyIryYx9ETOoYKJUBUUcxrks+KvvGSzkdiLcsRzdbfMhpPWtS2YgLGgIwcXcV3jQ8E85JdAAoGE1JIMVgWmXasbRddDDZU+tEUeKTcih7q6v+NaBVoRQYJqEOaysb7DsWgZPxgNDcu7XNmjV0NUd8VNJBwvK958DytrWmYtt7VvgjdjhmlgT5qbPOxjT06FjVINOVQDK33cabgz/Es9lvIeuELC6Owd/42VnZREsWuHCfTatVqg4zY6NNV0AgB/5Aii1Sye2HIChIBe8xMMddmNnx3bMSD0CBxuQIRtUAAAgAElEQVRjO+IQRRwOVqqiJ9fDKXSL7WGurGjxhDQpBk3EoQ/iyHynEQu5nQAAI3g4vvtTn89NRxvOYr/Dn40vqjoCBY7VvwfXuhO+ED1HM/KYZixgdyIQGp7eETRxyO0i+poKMRutkEOBbwnHq/tNe98HhKGZS3TDMF9IAC8c/MZfCryRVEUWnapIytGUYM5OmwurwRpzrA4dhzvMBhbGOLX430sz0H7RZ/hg9r9xN38tmpGCZqSo15towkFHHDa4U9THSXwAR7imqdtrmvvmqjzcGNPEoTWqHHNkUxXkcTwPBwV0qqK2MzCsXhMhQdSUUWY4iH4gEXGgxZEz7F2a54w134AJdEYfEhe0vuFG08dxxzBCEPYNj2kiDpO5enxuugvLTA/gZiwblpJMujtmevsm9fF6sVR9/IkwH0FJzjNywS4Y69cOybmj9R0SgA7/6GmupXwXdBRGcmRjTdN36vbCrEUxx+nQoUNGNAkA5OitxchpUrE0nJYo4pBGDPDWNArws2T76CQi1l7T/P1gpzskGNPEIRrhHjpADjX6GnHIdVo0P6y9LcMXdQiEBU1zqkyHCbOZvbjT8CZu8j8L1h1ry02LI0uNzZrnGEiw7H67T+dui7gP2uHHRJDzLA0+gG9n/lXdNh34ElKQaD2ObVsGKyOTjhsNyxFyt/TpfP0BHXFIbtmgPk6ZvAiXzc3Dh1fPw5lzJmG1OJ3Mc/8KDAXiEbZEItKDASVVkU1pHCosdjT4iXnNgsyjR3xeOnSMFTjiEAeFCKQlIA5JMcRBq4mo5knU4WgDaetQ5alEva9uwHMdKhxaxGEEQ8B9JQ7RAsnh9HOIXpCOqX8R75n/iJsNH+J88b9I/vCimAiCh0obTGBjfRRMBz7r07mV6M9MtgIc5O/BLVmxXSrE/uRFEM3JAOSow9yALLozI4TiZu3rW7a/2qfz9QcKcUiFG5Yu4g8/Z+FpuPW4IuS6LLiobBw+FY9UnzNVfgpIg/89xesR0jYM7by31bvxyFfl2Fbv7n0wBSXiQKcqvqccNSc6JyHdkjE0k9Sh4xBEXOIQST0kjjhoy/JdViOOKiRkoUEi7QkKQn6MdxSq25ta1w9mukMCnTgMELSPQzwPBxoa6+lh1Dm0UKmbU7gNKCl/TvM8565G0ld3aNwR6dRJVig2ImFs2tynfH9bRP07iyEL8xaxGBJYeAUOweIl6v6TBDl/fgq7AQZRq1NJ3vUqIMQ2yxooeEFEOKJ9mcFWqvsFWyZE53h1O8NhwhfCbAiS/F0afM0wNG3GYBGvDHaoIw68IOLuj3bizc31+J/lO/v1d6BUVdCpiu/9RNA5N33+0E1Uh45DEEnm2O67iqlTooiDMw7ZeOSsaVhUlAoAqJdS1f1sdz3K0uaq25vbNsUcO9I4pIjDaBRHAtE9K4a+JFOBIlA0IYy/GF+IO8Zc+Sm4VlkASdsNA0CSrypmPMMHYIiYI/UEpcKljCXOi5uliQDku+7g5LPU/UdhG2wI4KeU9aoCY7AdxvqhY9S0vmECQyIqQmqJphzKyLEIGlPwg0hcS83l8bUa/UHcVMUQ9+Wo6wqopLHNG0JTd9+JlycowAgeaZAjFUEG2NRFTJ+OzNCJgw4dPSGRxgGQxZOZDi15MBtYmAyxS6/JwGLp9GwAQL2Uru5nPfWYlTZb3f6xbdNBd5E8pIjDSEYcaBFfolJMBXSqoqrdP2x+BUrYuZipRwpkghKWOPwk+DD2iuPUcYb2PQDkRY3mWlbPgbiva2zcEHc/jR/r3AAklLFk0dksykYm/rCAcM58iEY572dkBFzCfYWjIhUcANAtEdW+sWFdr+frK3xUqkBDHJJjzYxcVgM+EclCaS5fDogD/65ESRqRiENHVAv06F4gPcEb4pGJDrCRCpiNZgsCojw/C2fF9JTezWx06DicES9VMS6ZXM9m5jo1z0ULI2koqQ06VcF56nFEahmYSKfNjlA7qr2xN3kjiUOMOMRnYZIkDblan+9HxKEgxQqbUQ5nSQD2DaNAEtD2HGiFC+VSHvZJhDhw3XIomk5TWBCE0UsW1hUCqds3NvQcAWh0B1DXFUAe04oMhlRmbBFlIxNvSABYDkIauZv/nfE19fE2thTP8EvJ+er7bz6VCPR7LGKb1MdCcmwHVpfFiBXCfPCS/GfBeZtgbBj4XEIJKlkGo3EICyK+Lm/TNDNriSIi/WlQ5g0JGvOndUnJ6uNZabNhZIfXIl2HjrGOaOJgMbBIo1oLzBgAcagHIQ6spw4ukwtFSRPVfT+2bRzUnAeLQ4o4hOJEHFq9IVz8ykac9cIP2DOEwsT+pCpYhkFJJimvGSqBZKJwlYshr98lyeetlYjAje2WtQz0gjPZQCoqgpIRHwqkC6KhYX2PXSM31MiCy7nMHnVfq2kc2iH/wby+sQ7/8+EOhNPiNy97y3yBpjTS2LRpyHwU6J4bxWyj+lhwxRIHp8WAdjjxrThD3Wfe9+GAz53IrXMwqYoX1lThzg934JJXNqI20pekxaNNTUR7R/SE7gCv0TdstpI7pbLU2fEO0aFDB4XoVMW4ZIumJ010xMFqjNVEKFA0EfVUxIH1NgNCWJOuONg6h0OKOMRLVTy2aj8q2nxo7A7i9g+GzkpY1Igjex9fkkV1yhwigWSi6lMXQ+5GuxBLHDi3HHHYVEMqLBa4SKSgSsrULOScrxmslyy60dgQcTybx+5W99U6tO5mq8rbsJ+LXaz5lMnYbp2HrVIRgpL8B8jwfhhatiU8X3/QFfFMMCGMbJBSz7gRh4hf/HLhKHWfqfKzAbfaTtRYrH0QqYqX1smkT5SAZZvksqyWKD+TvvYZ8YUE7G72IJ+RSWMIwA6O/A1NT9XTFDp09IboiEOeS2uWVkJp3ACgwZ1Yg2Q2sEgyG9BIiSMZSGC9TRrisKV9M8QhqPoaKA4x4hB7gV9V3qY+bvYMXW6Z5ii9RRwArUByqKynoxttKXCCEAdPHOKgRBwUi1MAKHORY+qldLTDCY+BlAdxXaQiAZCjHQfafAjxIjZWywTkSJZEHBqdZZrxjKkFt+05gLeSHNhpMkKZeXDiGbCaDQjChC0S8WgfTIqAhrKIFjBNYCNnlVgDhKT8mLFKCPFLcTbESD6R8zWDa98dMxYA6rsCaPJ2osK9P270Z6gjDtF6CaXMNCbiECFLNR0962m+rWhDkBcxnpFTODvNJoQin5GJNWGic3i6hOrQcSjBEVVVMS5Zaydt5LTLbG8ap3S7CUGY0CqRSAXrqccRqbPARpbsrlAnDnRXJnqJYceY7lURjXgRBwPLYOiK+wg05Zh9IA60QLKi1YsgL2paTQ92DjScdOtWNk7EobsOPM9jcy0hDpOspP6/UZIJQ6sxDw5ezn9znZUIjyPpi0dXVeCNTXWwmzh4QwJS4MYklhiTtKaUQb6HBQyOHbDkvYZmRsL9kJl0Bs9jWjCEQnMITabXYSvcj1sNPKbzmTjB58OS5q0D+kyioUQcJjBUxCS5EOBic/dKxKELDlSbJ6MwKBMhU/U38EelWT7e0YgHVn8M67g3wHB+nJ6/FHdMv0cTokzYkTQoIBAWYIkTsuQFEe4gj1RbbBlXVbtPs63UgkdHHNwBHm9sqsPfV+6Hy2LAsp/PQTrlIqrg8z1yBKYwQhw2W8iY0uSpur5Bh44+ICZV4Yq1Z798QQFejTS4u3FRYY+vl2Y3orIdaJBSkc7I12XOUwdH7jxMdE7GXrd8I/Nj+0YUOYt7eqlhwyEfcejLoj4Q0Hf7vVVVAHJdr0IUBAkoHwKBZKKIg4uKOHgZOUVSR5X3MGIY+6sq1YoDA8to8txNkcW90ZCr7qMjDqIk4Y1ImFxR8NPRBsGWiUZOPpYxtsOS+zaYqL4VLQYDVtlteLluOZrZr8FZa+E1hrHOasGf01JxIb8Vqxu/HnTZkZLvpysqpNT47aFdlGhpi5FETEw138SMfXDN67DmvwSGk3UGK2o+wrKK1zRj6FSFK0oQ1e6L1XD4wwLOe2kDTntmLd75sT7m+Yo2LXFQognREYeuQBh/X7k/8pjHc9/HKrC9IR7fV8rf+fiI9mOzmRAHvZpCh46+IZ7GIRq3nzwZ55bl4vSpmThnZk6Pr6foHGok4hjJdR0AgFGjczikiEM8cWRv5kwDBb1oG/pATgwsg0kZlEByCNIV0R06FdAaBz8nEwcfLAiaSOqhupIs9DNykmD0kYoDJeJQy1LEoZMQh4ao5mIAsIDdpT4O58zD9FwnABGW3DfBcGR8WqhvQa4GRsQfNv0a96y/Hd3h/rkh0lDEkZqIQ1p8lu6i3NzWMrPUx8b6tQBPulp+17Qalpx3Y8jQi3uexXdNpDkUnapIshgo8iDB3d4EJqT9Dby/tQH1XQFIAP7yJSlrVZpi7W/1asa7AzwkSYrVOPi1GoeV+1pj3uv6qk6EBAlmhJDLtCPIABstNHGYEXOMDh06YmExapfRPFcscXCYDXj4vJm4b3Epkq09R/IU4lAuUdff9r0AtMRha/vmg9Zm+5AiDvFSFcMVceiPj4MCrRHUyEQc/Bw5p9dCmC7fWa0+npbj1IgfFWFOlZSt7lMYLwBUtGrvfBmIOI0jmoRw3kKU5bmwaNYuGGzkbtdfdxFMFdfh5QNh/KqBQ4FwPKalzECOYTYCDeciqXYpLunqhoH6bNe3rsOv198JPz+wdtRKxGEcQxZPKbkw7liXlZCa9XyR6jvBCEGYar+HIEr45+aV+OOm36mkQZJIukGChP/98T5UuOW7fTriYDFwSLWbkItWfGO6DcesOBppL06F4+vfqmMORKUieFHCY6sqcMwT3+F/P98bE3HoDobltEdUSkT7OhK6AnxM2mRdlZyCKogIIz+12+Hm5Pdi4SyYkTILOnTo6B3R7pA5zlji0B8oJZnlYp66zxAhDjNTjwDHyH+n3eFubGkbvLvtQHCIEYeRS1X0x8dBQWnW0AokE4ojKY1DkCPVHG4zIQ7OIAmF240sOIo4NEUiDpVilrqP6zoASCKCQhDlLVr3yyOZPciNpDokhkPX+JPw7K4nsCX4b3VMuGsmeHcZ9kr5OM//N/yp80Hkc5fjyaOew0nOuxDunIe27iPxm/YOvFvXgIU+QhR2dm7HI9v+t0+fSTQUjUM2lYqBMzfuWNo/vi3AIFxwnLptLl+Of/+4Ea/V3g9Bku/wJcECX+XNCB24HTaDTDL8gg/3bf4t/LxfE3GwGFmk2U24w/gWClhZW8BIIqzbXwHrkdMoNqM2GrOjwY3/21gLXpTw/tZGfLO/DROYBrxgfAS/Mfwf/H4/WrxymoKBiGR0g4GIqg4/pjIH8IbxAWwxX4N15htR++N/NK/9Q0TQWhiJxLyZRH6biwsXw260Q4cOHb0jw2HG1QsKkOkw4c4TiuO6QvYH6RGnyb0SIQ5cZwUg8rAZ7DgyY4G6/7O6/8QcPxI4pIhD3FTFCGgc+kwcMskiXt7qHbTTZSJTKzriEDQSZW6XiRCHlBAhCi7WB4Yn6YSGSMShnCc5NkYIYlXluzjz81PwZvtNsOb/C7aiv8Fe/FeE897B+kiYO5y3CPfveRzvHHhTPdZpdOHi8Tfhp3OICRUA1HXK57SaZAYdhAldbDKKwjyebWrBWWmkLHJlw5fY0dH/Es3OSMSBNjmSHNlxx9I6BHcgjMDEM9VtQ8V/8UrlI2raRRIN8NdcDjGYg6A/EzdM+q3q7FbjrcbTOx+LijiwKDG24iw2ti0u11kBABr7bwD4eEeTZpuBiH8YH8PJ3CZca/gE13ueQos7CAYinjA+hR8t1+FJ45MwI4RnjI/hKG4nXIwPWUwnJm5+QC0rbXAHUN0hE7PxTBM2WMzYSqUpLpx8UdzPR4cOHfFx3dGF+OS6Bbho9rjeB/eCtIgwulLKhhBZohkxDK5Ljt6eMm6xOvabxlXw877YFxlmjHniYKOU6YmqKmgMlYNkf30cALljmjKfsCDFhJ77i75oHMIGQlY6jWTBTOaJ4VOqQEpWBdaEDsjHtIRNEGwyeeABPLP/XwiLYYTQAYNjLzhzC1hTG8odXlyVk4VbMtPxRHoGvm/+Vn29JGMSfl92P25aOAO/Ol6rLTAZ5M/CRuUIm1n5fAyAO1wLUOIi1Qwv7Hmm32JJd4CHBUG4qCgMkuKLk1xU7lGQgI6c49V0xYdmgLGS9A5ffx4uDe3DnYY3cRn3GVIC43FJ8WXq8ytqP8J2N9E7mA0czvS9AwMTh9xG0kDRZZoKcShkGvCa8UFUWn6GKSxpRHa6uBLZu57HUnYtlnJrAQBncOvwuulBjGe1LdLTQzXg2mQdytdqibIAq6sKt2SRipuZ6TNRmloKHTp0HBwoEYcgTKimBZIdcrpiYebRsBvkCGFA8GN149cjPscxTxyyneROqS+pikQlcv3FQCIORo7FxHQSAh6sk2VfNA48FXFo50hlRYpAcv7J1OOwNROyMbYEb5BXHRa/tlnREiblm/Gwym7DK51E6zDROQnLTngfs9NJZ7fbTyDk4cr5BQC0TmpNDJmjydOAa0puULe3tv+IH1rW9jgHGpIkocsf1kYbGBZwZMYdn2Q2gP4mO8IcgoWn4IkUF/6UTgxZwu4ZuM+/Hn8yvoKbDR/ifuPLmPjj/bhi0tWYkjxNHfeN+zkwRjlFYjGymOqJjTYAhDhEV1rwogQbAvin8REs4uI3Gptb+RSeMD2l2TeHajRGw7xvOd7YVIdHV5XD4NwM+8SH8XJWNTxsxGIbDG6ffUfcY3Xo0DEyoDtn7tP0GJL/rk2cGcfnnKjuX92kE4d+gxai9CXiMFQNpmiO0p90CO3nMNjKing+DmaEYGbIAsSbXepjmjik0cSBJ46Kr7ucSJryG1gLXkBACIJ3FQIAljm17meSaEC4cw6ObpyA2YHYKguW4XDPzN/DarBp9l84Kxd/OXMqnjp/BuYVyH0RaOKg6QrXXYfZ6XMxJ/1Idd+Le57ts2OaPyyCFyVkgRAH2DMBNn5lB8cySNKkK3gsS8vFC8kuiBEBrCSYkN08F+dz2j/Wws41MDAc7p11H+wRvUNY8sOc/gUAIIkNISlMPvNPTaeQ87rlEGQ8R8k/GF5FMdug2SdIDGqpz4mGBED5dOqlVPwtfD45z96P8PjX5TBmfgzruDfBGrVE8Krs03BEhtbxU4cOHSMLO00c6B5DEYEkAByTfbz6eGPrDwgJw+FWlBiHAHHoOeIQDX94iIhDP30cFAxli+14EQfaNRIARBOJOLQwxP/cDh8ckWZYSSGZODRzHJ4yy8cb7BUwujbCZx+PAwYD1lI9DHwHroVn7x8RaLgAV3qb8HJDMx5qbkVepILDzJpxXcmNcc1JOJbBiZPSMX98imqWZDMR4lAr0l3hZGtsOuqwv3sfVjZ80ePnokApxaQjDkiKr29QQDeg+fkba/BE4yp1e3w4jDcbq7GKuw9cVCmmXewG429Fji0XN075pbrf4PoRjKELuZTddVjisJZq3811HoAkSWiPSlU44YkhKCJjwKP8+fh56G64JULKGjkOD6Sl4OiCPJyel4vV5iTcGLoN7whE4GnxVOG0tL/ClPqduo+RJJQGQ7jWB1w4654ePxsdOnQMPywGVk1/76MqK7gOUqI9K7UMFk6+JgeEADaNcNOrMU0cclwWzMkn3fwkPgC264CmtwAfRSYU4jBYY6H++jgomEJFHPa2eBPqFPo7BwW0vkFiTWCNZMFvhwsSdbetLKiOsLyoveZMQhjkNU1pq9Fly8NbVLTBwYyD4J8ASAacOcmGeVw5GABneH14fdbD+HLxd/jk1C9xQdElfX4fdMShmiIObLdc+THZVYrjc05S9/9r7/MIi703wXL7+y6MVEB7OZjSVoFnZHJnFUX8q6EZ00KJ7WKVkqmTx52GTItckcIwIiw578Iq7kNX5HdSK6VjW4CkS1h3FbxBHqGo3+p09oBKUHiTC9t/tgdt1+3DK4YLsF8ah0tCv8UHwkI8Lx2HpblFeNOZhG6ORZ3RgBtzUrEzvQJcuhWfCzPRyHH4tzMJ36ST6pKpwSA+qa3H2/WNuLL45+B0p0gdOg46GIZRow4ajUOkqzEgpyuOTJ+vbq9pJjcDI4ExTRxW3LII9ohPOAcBT/v/B2mvLYL92z+oY6IrLXwhAX/4z26c/I81+HCbNgTcHwzExwEAitPtKpsM8mKMjXB/EJc4UBEHyezS2Br7eQminSycSomiLdgCD8Pg7ah0BGtqwxd8Gz5wUPu7FgARJcASxz4wkrw4i+Zk8BkzwDAMWKZ/Pyta4FoZJloCrrtWJYFXTroGbKR+ucFXjxU1y3t93c64EYeeXdtSI+1wWUsdTGnEMfKKrm5kCtpo1Y9iMX4QS8h8O+QcpIE14EKKOBkce/E89xoWjc/H1dmZ+NKSin081Tsk7EVXW+xvcTpDuXVmzkSWyw5wRjWdskOagNvCN+LRTCMChqhQJSPBnP4VujL+hNsnduLkgnF4OI0YgBWGwnimsQX5vADJYEFgyoU9fi46dOgYOdgjUVhNl8xgJxAm68VRWUerj9c2fzfom+H+YEwThySLAaZIA5F8phkTJTlXbN25TF1wonUPX+5txYqdzegK8Hjgs/gisr5gID4OAGAxcpiQRgkkB6FziBetoCMOokVLHIK8CNFBFk7FZtrhqcS3NqsqkqPxVONL6I58xlZRBNOgHC/h6Ob/U8eF8o8B2MTtYnuClUpVVAnkD4Xh/WCCst9AvqMAp+edoT736r5/oTVAwv/xEM/DQeolVXHOzByYjSIsOW+BiVRAiKEULOqQyRMvsXifOxU3hW7FI+n/i720SUsH+T0tzlsKixRLUtZZLXgi1wuvsxy8iehPgm0VMWNnsIQ48JnEyZEWTxmT18PgILlPwTcektCzAU0mz+O5pmakiiIkMPAc/XtIlpQej9GhQ8fIwW6S/8abkQKRIddHzkP8d+ZnHKWWgLcEmlHu3ouRwpgmDgDpPOaWyGLM8D4wITnEHF1F8eXenhebvkLsZ5MrGhqB5CAqK+JlOZwgjFQyOTWNtPa1ehGyUhEHtMOKACzeGuw0EfczRtRGHhSc4fFiQkRoeJFtMxxtP6rP+WdcOeD3QUccOuGASAkq6fDcZZOugomV59kR6sA96++AJ5z481NcIzMZ0j68t4jDMcVpuH5xHTgL8VAINJyPG4J34YHwpXhg3HM4+roXcccNt+PGnxyBfbRJSzshDlaDFRNDdyLUfhQkUZsCkBjAkvsmPnWR70KkiMM4lwX3LS7ByS4SheDTCXFQBZysD6aMT9X9Dn4mfFXXw1N+N4LNJyPFRKIaAADBDM5TCK76p/AYZyJYeDI6L/gYgemX9/iZ6NChY2ShdNwUwcJH/R2zFHFIMadqqrhGMl1xCBAHedHugANhkAWI9coX/mjB5FC11taUY/bTY0ojkBxExKE3jYNodsFCEYfKNh8+rCKTzWHaMYmpAwMJu8xkcUsOngQxTESVCi5xe9SeDzcb3lf3BwtPAZ87b8Dvw6rxemcQspPFne0mHTczLBm4vvQWdbuiuxz/3PtcwtdVxZHouzjSHXLjrcpX1e1Qx3wIvmLMmT4dV978EG46ZzEYhlGjXbTq2dCujWAJvB3BprPg2fNH/KfJgOcam5EaSXcwjIT77EFURtwiDW5izZ2fbMWSYissHrIvTEccIsTBnPkZWINMFK2cFadmXA+AAUQrriy5Cu/+5EMsO34FPPvuRvfu+9G99z501lyPvcHZeKvkSbiXvAQ+U6+i0KFjtEGJOACA20TrHLTN7xZmLlIfr6H8c4YbhwBxkN+CBBbNEgm3KsQhnpskjYEaQg3Ex0EBTRz2NnuGZA4KetI4AEB5gITHs5l2lLA1kADsoiIOTrYIvFvbq2CB349J4TAKmUZkoR35of3qc975dw5o/goMHKsSQAAI2KjmLlTEAQDOLjwPF074qbq9omY5WvxasyMFW+vdACRtqsKROOIgSRKe2fUEPHyk2kU0I9RyMgDglJLMGCtZs4FFOVVnzfpbwAQISSE9JFhkBeqx0B/A8w3NYASZpPkZEbdnpiMMwOIl5lKpdiMMrdvVbdHkhOgcr247LQZwjp0wpRBPi8smXolrjpyJO04oxu9OnYxfLJA9MjJtyciyZgOSNuqhmMzo0KFj9EGJOABAp4ESUnu0xIHWOezp2t1r+naoMPaJA7VoN0mkwoL1NUKUpF6rFkIDNIQaqI8DAEzOdKhGQ96QgJqOgTVw4uMQDk1VhcWlSVUAxE4akCMOk5laNBg4dHHkh5puKES4S0scLuuSF9PxTBMWssSMSEjKh5A2BYMFna7wmklUgO2ObS991eRrkW6Rw3dhMYzXK/4dM6apO4i1BzrgghcWyteip1TFewfewqd1K9Tt03IuRklaNi4qy8W88ckx400GFs1I1pRF0iVTgUgFTzrcMIqy10VJOAyp4Wx1TLnJhJddTiT5CUFKtZlgaNqibvMZ0wFKgMsZ3bDmvKNup3AFOLfwQpgMLC6ePQ5nTs9WS10B2bE0GoqtrQ4dOkYf6IhDG0enKuo04wodRcixkhutdS1rhn9yOASIA30X2BQVcegLKeiL90M8DNTHAZB9C8ankjLJgQoke/NxEE2umE5tjRRxyGbaUMLUaKINOdZcuMwuiMEchLunAgDm2AqxyC8vfBOYRiziyN1wKO9ozaI2UNB2z10mqiunpzZmrIkz4dJikpdfUbMc7cE2zZgVO5sgSsAkhhwvGh2ANb4IsNpThWd3EwfGaSkzcNusK/Hvy2bjzhMnahZiMg8WAIP9VPtbQweJxCgRhwKG6CV8XBK6u+cg1E4a1TyX7IRHJHqGVJsRxjriMslnkVa6gshjU+hpMJEUhSQacW3xvTBxiYlAUVpswyo94qBDx+iFnRKMt1BuupxHW33FMAzmZZKePhtafsBIYMwTByVVAcQSh788uvIAACAASURBVL6QguAAG00N1MdBQckQtNju1cfB7MKULAduWlSo7qMjDqmMB0ewFRph5CTX5MiPlkGg9jIcY3wEj8z4vfpDKWIacB5HejCE80iObTBIoioF2unQXHddvOFYnLcUaWb5DyoshvFh1Xvqc7wo4YNtshZjGkt0AkL61IQk57ndT6u97dMtGbhv9kM9LsYA1GgOTRy4ztiIA93S22uRxwZbToUxIkINsix+mWVDUvI3MGeuwKruP+Pe4E78MjMdV2Vn4mf+Dbh01fm4evXluOCrM9HC7yHnaDwLx43vOeJTHC/iYNeJgw4doxUO6nrYAMpN1xMbgZ2bTvRlm9s2qNex4cTYJw7Uok1rHDhvU59IwUA7VA7Ux0FBaRZpPjVQgWQ84jApiXRYlMwuMAyDK+YX4OQSOdzVgmSEQe7uXYwXO80UcXCWUD9aBmI4FVJqCTxWuXqAjXJMDOUdjaEA7djYzJLQHJeAOJg4E84pJHbKH1a9h4AgR0X+s7MJ9V3yY5o4hNOnIR42tW7QCItunPJLpJpT446loRDGCpGkP7gOUh2hVPRoDKiUVIloRbD5HHV/s8EA5KyAKe0bVAY24782M76y27DeasG+QD0afPWo6C5HZ4hUiIS7ypAiHBWjY4lGvIiDnqrQoWP0go441Eu0t029xuAQAGalzgYXKdl0h93Y17UHw40xTxx6SlUkIgWlTLXc0RDuATe9GqiPgzoHKuKwp9kzIPOOaOJw/dHjkWsmRkCihQghlR+iAA47jNPV/RKA7RRxmOwq0dyN7m/14snVB/CGn/SLUMCnTYFky4jZPxDQxKFBonJ6/laAj68BOSP/bFg4ORXjDnfhs9r/gBdEvLiWCA0XWEmqgs+YHvMagsjj6Z2PqdszUo7Acdkn9GnODMPAbGCxn/Jr4DpjUxU5lDjTkkLKN70d03CXd2BujZlsGaZwl+PvZ8e+p2hMSNNGHFwWQ4zQU4cOHaMHdMShRiDEgeF9qreNArvRjqnJ5DqwsXX9sM8vfrefMQSNOBJa4hCPFKShC++Y/ggHE8AF3NfwhI8Z0HkH4+MAaFMV7gCPBncQua6ejXuiQTe5OiLXiV8sGA/DHre6T6IaXNH9IL7BbMzCZgBArcGATkoYWZo8FaYQed19LV7sa/FiMrMA15hJCSYA+MpuwFCBtnquF5MhMSyYSDMrztMAIbko5hinyYnT8s7AB1WyUPCdA28iKXi0Gm2wMDzyeBJx4NNjF9nl1R+g0iNHCRgwuHHKrXH1DIlg4ljsF6hUhbsKEMIQWQMVcSDEgXPmIj/ZgppOeY7HYzzGNa3B684kbDLZEeBTcQXjRYqnHiZJgqnwFBgnngWbwY6OYDu6w27MTp+LCUmxfUASwWrkkOuyqJ9Lqp6m0KFjVEMTcQjZIXFmMJFGVqynAUKUYdvc9HnY1iELqn9oWYtLJ/58WOc35m87OJZRKxRiIg58bK7nGHYbHIx8AZ3JViK18oMBnXcwPg6AbOIzjiIKA0lXxCsJZYKk46FIORM6KJXup2FSu7+Vijbk2QuQZHTGDW3vlfKxh3JJDBSfgeDkc2LGDRR0V8rOoNYaO5HOAQDOK7xQdU+r9Vbjm0aivzg/zwM20tNCYo0QUidpju0KdeHlfS+o26flLUFJcv8qREwGFtVSFgQp8vmLPDh3tUaYm02lKgR7toY0VktZONHnx4uNzbinogCTK8/CXXW7cU2XGz93d+O0SZfjuJwTcWTGfJyStxjnTbioX6RBQREVdUjXiYMOHaMadqoc0xsWNNdDztsYM35eBhFbb+/YirZAa8yYocSYJw4Mw6hhV7ockxFDEP0dMePnsbs124U7nkwYCu8Jg/FxUFCaNbhOmTFzEMJgw1pxpAI64rAjQMQ228yku2ipS140kywGZMRR3f82fBVqxAzUpy6A5/iHhqSaQgGdqugK8BCTKEfG7tjKCgXj7HlYlEU6QG7wLANY+fucbSIpCz61BIgSO76093l0h+XP3W6w4xcl1/d73maOQQhG1NDNaDr3IxCmiQOJOIiOHEymiMOuIPkuxrNNuMPwlrodzjxCU1ExGExMJ2QwK8ncw0gdOnQcbNCpCk9QgGgj1wnG3x4zfrKrFNlWOWUqQcI3VFff4cCYJw4AEam5YYfAkouiYgJF40hWKxyxBBphqu2/VSddsDEQcSQQ3WJ7ABGHqHQJE3JrnpfiaBwUPMHLXgLbqIgDbV9aHCfqsEEqxTGhx7Hn+H8NeW8DOlXRHeAhOEj4v6eIAwBNQykv6mDNfxlgfSihqg/4iDBSlER0h9xY2/wdPq7+UH3+solX9kkQGQ1TvMqKjnIEItEuBqLGuVK0Z2sI44Zu8jnOZ3fjGKrU1Tv/riEjZ+cdkYNMhwnJViPOn5Xb+wE6dOg4aKB9HLwhHqKFXJtYf2w0gWEYHJ9zorq9quHLYZ3fmNc4AHKe2QsBAIOQNRNWbw0AubICINbJKXBjEhu7CMUjGL1h6CMOskCyP/n16JJQlkpTSAwLyUhe3xZFHP7BnwWWCWOHeTsQaaU9JXmq+nxRug1rq2IjNgBivCGGAnSqwh0dcfD0TBympczAxUWXYlmF3HTLYKuCfcLT6PCQP7CGzGl4bdeTWNnwOVqjwnh59gKcU3jBgOatlANXSDk4KaIb4Tr3wxOUiUMaumFkSMpMdGSjxELI7Xp/DiQLCwZaPU44Zx7C+ccOaE7xkO204KNr50MQJU0Jsw4dOkYf6Bs9UQJW1TM4NbLN+tviHnN8zknqNXB7x1a0+JuRYc2MO3awOCSuILRdsd9CPiiTV7vgREcbFCgNsfoDWhw5EB8HQCuQ7PCH0dLPPhqayg6G0egbYHFp7lZpBgsAAZjxd+5YCJHySiNrQrGTaADiRRwUKK2nhxIuTaoiDCGJsnLuJeIAANeU3IjFeWeSY0xtuCGVwaU5WfhVZjouqvs33q58I4Y0WDkb7p75WxjZgb0nxcuhgqqsMHRWoLxVThllM+SPXDQ6IJmSkGozqReGNriwO+X4mNf1zb5pSFNBgBwZ00mDDh2jH3SqAgDKfeRmLV6qApBL6XNt8nVTgoQVtR8N2/wOiasIfTH0WPPVx1bPAc24uWz8tqMDIQ6DcY5UkGIzafLNfRZIigKM1V8jxUOaKrFsNHHQWiRHpyoAgLPWqI8nOSdpFs94NsUsI5d89icq0lc4qVSFO8BDcBDikMjLgQbDMLh0/C8RbD5Fs3+rxYwv7DYExGDMManmNDy64ClMS5kR81xfobR1rxC1qQrFDZQWRooOInDKpL7390zEghoABFsmQuP7VhKqQ4eOQw8mjtHckLZLJHLOBuJHHBiGwen5S9Xtj6s/BC/ycccOFodMqkKB20aaAdm9VZpxxUys6xYwMOIwWB8HBaWZDjR1y4vaniYPji1O6/UY26Z/wL7uL/gZWLzJ3I8d0gRwjDZVEU0colMVgJY4lCZrzZGia/8/umYekq3GXs2GBgpaHMmLEvzWHCjvgPXUA5IIMD3z3AZ3EKG2EyH4JmBK9kuosmgjOC5TMq6ZcTWOG3c8mjs6kW3NhtUQS5D6g3gaBzbQgdpG+bemEUZSyuisJDMq22Tb6C+6C/ATsRTzI8Jd3/z/6fW96tCh49AFwzBwmA3o9MtVYa00cfAlrphYnHcGXt77IniJR1uwFd83f4tjs48f8vkN6dXppZdewj333KNu33rrrZg5cybKyspQVlaGc889N+5xK1euxLRp09RxZWVl2Lp1a5/Pa6BSFZ1WQhySoogDbf1bTt0hsqH+CxMH6+OgoCSrny22JQn2dX8BALAQcbPhg8gctKWYtDAS0Jb3KOAshDhMcU3VPGc3GXDbcUXIcJhw7VHjke20DBtpALTEAQDaDcQEihHDYH3xO2DSUHwKBH8hXm1sxdONzZgdCGCqJRc3T/0VXjvubVxa+jPkJeVhQlLRoEkDAJgjpLUVTgQ58l0KrXI0KLqiQgEdaaps9+Hm0K14RzgW77muRGDKxYOelw4dOsY2fCESLWgHHXGIn6oAgBRzKo6lDOyWU1b8Q4khiTgIgoB//vOfePTRR3HWWWep+3fv3o1ly5Zh6tSpPRwtj7vkkktw7733Duj8dMSh00JSFUmBWnAQIIADIGmIw26pABMh3xVGVyP0BYP1cVCgrazoPfLBte7UbM9ndwGQdRZMkHof0cTBGPVVsz6wZvJ5lCbHfkeXzs3DpXPzYvYPB4wcC6uRhT9SxtgpWCCaXWoUhe2u09yx727qRpAXMTPXqaZOFOKQx7QgVerEsX7gWH8ArYu/gGTtf8VEX0AcGBm0WQqQ65W/n+xwDYDiGA8HBVkObUlkC5JxZ/h6nJuVg2OGIRWkQ4eOsYUQVbpHpyqYBOJIBWeNPxdfNXwOANjUtgE1nmrkOwqGdG5DEnG4++67sXHjRlxwAVGme71e1NfXY+LEib0ev3v3bpSWlg74/CZq5W4zj4MUMQTiJAH5jHyn6oQXSQzxa9glkg+SGUDEYSiqKgBgChVxaPaE0ObtWSBpPvCZZjuV8SAbbeBYBixtRdpLqoJOU7hMyaqo5mAiRueg8XIgOof11R247LXNuHrZFnyxVyY/B9p8qtX0bIY0muJdE4aNNADa316rmfymihm5i914A61xiB9xoBEtitKhQ4eONon0NmLD3h69h6anzMQEB3Ha/aj6/YRjB4ohuUrdddddyMzMxJNPPom6OvkCv3fvXthsNlxzzTXYs2cPSktL8bvf/Q7FxbGud7t374bH48Fjjz0Gu92Oa6+9Fuedd16v5+U4FikpNljNZMHhrHYguQDolNMUE5hGHJBycNEkAJG1MsSYUU0Z9hgFL1JS+he2ZqgoR5LD3O/jFaSk2JDhMKPFI+sc6nxhTMyjFn0+AHRUgmmvANr3g93zdsxrHMNtA2c5AhaJmD8xtpSYORk5Ru0YShOHGenTkZqauIpipJBqN6l6D9HAgkstAFp3AADsQjNskffzh+fWqcf85uNd+MmMHFzxxmZ1XxlLiUYL5mk+By7yvQ30+4pGEtUsqp1KkxVF9DR5hi5ATlPCljUe1sh5i3PJHQSNrBTrkM1tIBjqz+dQgv7ZJIb+2fSMgXw+1yyagBe+rQSgTVUAQIrJD7gS6+EunnIRHlr/EADg0/oV+J8Fd/Ta7bc/GBLikJkZWyvq9/txxBFH4O6770Z+fj6ee+453Hjjjfj4449hNJKFXhRFZGdnY+nSpXjmmWewY8cOXHfddcjLy8P8+fP7dH66YU9IECGlFoOJEIcipgErUYYskeTIO01Z6A5SX2BwsK6NgwvczMyxorK8EoVMI0zrfwBb3gW0V4Bp3w+468Cg5wZYx7DbsI5jgUDiiAMg3812+ORVzOAgpakz04+IGXsw4LSS30WnPwy4SNqJ6apVPwWFZCn44UA7vEHilVDGkoiDNG7u8Ew2AjOl+2gykfkqQtxUgaSD1M6YAHIS9CWJ1nro0KHj8MTNJxQjy2nGAyt2IwQj3JIVTiVq7msFXInTyKdPWIJHNz2KgBBAd6gbm5o3YkHOUUM2t2G7Si1cuBALFy5Ut2+55Ra88sor2L9/vyYtwbIsXnnlFXW7rKwMS5cuxVdffdUrcRAEER0dPkAk5jld3UEEHIVQaEFRJGScEiL+3p2GLLglQhwkf5f8Ov1AKEwWqqA/1PvxIg/WXQNDVyW4zkpwXZF/nQfwgrsGrDnyHioj//qB+ewufBcMg/d0QOGUotkZMyerkUMHwmAMXZqIQ5lzfr/f/3DARhHAxjYffMYsKImccGsV3AnmuLOGECYzQpjBVSmeVnAnTQNPHacw/qF6vxLVD6WKEtwWMM1wwQOzSM7TKaZAipzXEqclOgCwym/6IGGoP59DCfpnkxj6Z9MzBvr5nDUlE899U4EWTwjtklMlDp6mOoQtk3s4kkVZ2lysaf4WAPB5xUqUWGJvEDMykmL29QXDRhxWrlwJn8+HJUuWAJAFlIIgwGzW5nabm5vx6quv4o477lBFbqFQCHZ730PntI9DWBA1nRQnRIhDOk8iDm5TNjywqtuDFUeqPg6iANZTHyEEleT/zgpw3TVgBlhTK7EGCM4CCK4JEJInQEiZhPC4o5D6f7KzYBbTiVSxNdYAKgqKl4PBsUvd5zJkoWgATZOGA/TdtjvIQ0ilvRxIvwo65QIAVe3kj/HOqT5wFfLnLHFm8Gn9a1rVX9DC3Do2BxLDgZEEGBkBx7Db1Ock1gjJSkKLNhOHJLMB3UHtb0KPOOjQoYOGzaiYxTlRCNnlOJF7JI35GUepxOGH5jXA1NuGbE7DdpUKh8N48MEHUVpaivz8fDz66KOYPHkyJkyYoBnncrnw3nvvIT09HZdffjk2bdqEFStW4I033ujzuehUxY6GbgiziEgtN+LclyYQW+lucza6qYgDIwQBIRTTBKkn0D4OpTWvI2Xjh+C6DoAR++f+qECQGNRKGaiUcjBrxixwacUqURCT8gA29qtqM+YgLRwR4fl39ujjAFDEIWkHmbtjwbAYOg0EtHtkdYcf4mQSimPd1YAkAQwDI8siLFB3+h0yC2ch4oy2F9X9fOYRADf0Lpc06N+eXzSAT5sCY6vcb2IxR7QYoi0zxpshK8kcQxySdHGkDh06KNgj1wSNCVRfiEPmUUDkUl/rq0GttwZ59vyeD+ojhu0qdcopp6C6uhpXXXUV3G435syZg8cffxwAUF9fjyVLluCTTz5Bbm4unn32WTzwwAN4/PHHkZGRgQcffBCTJ/cUhtFiVq4Tn+yQicHX+9vwWaYBF0aek+voJaSECXHwWnPQTUUcALmyoj/qe8XHYTzTiLI9f+3TMRIYiEnjSOQg8j/vLMQpb9SjLZK6f7poBuaN772J1AHzFJU4FPh3Rvk4xCMOBoDzgrPvV/dNdw1d3muwmJpNwmZfl7ei4sgSzIlss2EvWG8jREeObDEeJscdiEQcruA+RU7XJnW/f8YVwz5nOuIQEkTw2XNU4rCE+0F9jq6oUJCZZFKtqRUkWYaX6OjQoWNsQamI01RWJHCPpJFlzcYERxEqPRUAgB9a1oxO4nDLLbdotq+++mpcffXVMeNyc3OxeTNRwc+cORNvvfVWzLi+Yun0bHy6uxkbauSF86WdYZU4WJgwkuFBcphoHHyWXHhhgSgxYCO9GphQd7+Ig5KqyGNaYp+zZxNiQJME13jAEF8UNzHLh7YqOVe/p9nTJ+JQaZ6COZ6vAAAFvm09+jgA8g/QmLQdDCPrKUQ+CZOTpsWMO1g4fmI6xqdYUdXhhygBz21sxzO2LHA+mfRxHfshOnJgiOq3oDSU+jn3qbovMHEpghOXYrhBRxyCvIhw9hxYt78SM472cFCQn2wFoG0klhTHqEuHDh2HL+xUqkJBb14OCuZmzFeJw7b2rTi38MJejugbDglfW45lcNtxJE9f4TVBohboi7mVsPNEQOe35UICCw/IGLafttNKpsIKkprgk4vRcu1etF+xAV1nvw3PCQ/DP/sGhIpOg5BWkpA0AEBJJmGTfW2xvd9M8vd5vp3a6guqf7u6y8TB4PyRzLdrJqzG0XOHy7EMrl1ISho/39MCj4Nsc51ytUQ824xxaMF4luhYvAvvHfImUfFgjoo4hLJmxx1H96lQcPbM2CiEnqrQoUMHDSXi0N9UBSB7OijY0bmth5H9wyFBHACtpXKQlzR3ePcYl6mP+ZRJCNv+v717D4+qPvcF/l2z5j6TZHIFAoQkBBJAIAEEBEQQRGutFxS1tW4VpSqP4NnaA3YrPbunirY+ylZPaymP9ehpu3ue3aPyVHY3ar2AFRUoiAbDJYQECJDrJJPJXNf6nT8msy5zY5KZTDIz7+d5+nRmzZrkx2JkvfP+3t/7GwMAIQWSgwscgjUOJshLA5khF9ANbR1zzWBbTwNo0VXAw8JvNIw3AIbwalmN1g6tRV6y4eutlXZ3HC1WTC1G5cA+GQzAP/rlpb58d2CKxaVY0RJ0BS931PTnlUPMSU1DK9VSYL+IfvMEtLPwbI9oCQ8SqoosuFvRmTPPqA3LphBCspu0k+4QAocZ+ZdJjzvc7WhzXYxxdvwy5l8pY8gN8IjDGnYO05rRu+pX0GkDN1tVgeQgA4fgVIWRkzMOTGuKdvolKVtPt3S70Oe59AoMD9PhlGJzJYmlKOK37Tbskx6L3kKI7gkwakdXajw067CnW56y0dobwRiDyxshcNDIgYNv/KKw14dL6FSFwyPgH+KUsPMiZRwAYP2VFVg5tRhGrQZrFya3LSwhJP2Z9QPFkVDUOETZWjtUgaEQY03yl5aj9m+SMqaMCRwMITfAZl/4t77+ugchFE2XvmUnsiQzWBypyjgkEDiMtxlVW1+faHfGODvALzJcYBFqISzhDblEJuK072Ppua+nFhqOG5XL/5ZPKcKU4sByXOWuk3z3SXj8IoSwFggMC0cqcFC0nPYJDH1eP/4m1oWdJ0aocQACe4w8+71p2PvoEvxgbmr2BSGEpA854yDf0+KtcQCAGfkzpcffdCdnuiKDAgf1H+UCCy909JcEGmAE+z6oMw6D269CkKYqkpNx0HAcqksGN10hMuBihMCBWcLrGw53/gO9A0tSGePA9c3HvQvKkGcaPTUOQRqOww8HUviNiqZKfF8rXP3hAV4Z14bxnPwfkm986laKKFdVeAQRDrcfO4XFqgZjACBEWFVBCCGXYom0qsLXF9iOIA7TbfJ0xdFuyjio6HgOyuR8xMChKLADZLCgTbkkUzPIttNS4MAp2h/rhh44ACF1DnHslCmIDBcRKeNQHHZo15md0uMFJQuxZ/0NeHhx+ZDGmQpXTymCRc+jFYVwMbm/htjZGHbuMo2i4DN/CkTLmJSMEVAHrF6/iD6PAA/0eEdYrDovlWMihGQOqTgyZL+KeKcrlHUOJ3qPwSN4Ypwdn4wJHDiOU803hwYOXl2eVKCm0wZCDAdTTFX4Bhk4DExVGJOUcQBCA4dLZxwEkeFihAApNHDwCl7sa/tMen7DxBvlbpejlFHH45rqYjBocIrJ39b1578MO/dqReDgnXR1SsYXFLpPSrCh00v+1ejhAt8QfGPnAXzk3TAJISSWYMbBCx2ckDOZ8fRyAIDKnCoY+cCKPoEJON7TkPCYMiZwANQFkqFz/725NVLBoJxxUExVDDHjYE5SjQMA1CiWZJ7u6oc7wuoBJT9juMjCGz2xkMDhm+4jcAuB7opG3oj5xQsTGmeqXDctUKuxR5SXFBW0/KfqHBPcqsLIlAcOvLo4MljU2ok8/Gvhi3Asew491/8upWMihGQOs6L2rZsbfC8HrUaL6jx56X59EgokMypwMMTIODjMcsV68FtiHxv6cky5j4MycIjepyEeZfkmKfgR2aULJAMZhwhTFWZ14PBl++fS49qCOdCnybff0oEdJN8V5EDH1nUY4yD/B7NIUw8DF2gjKepz4Bs3P6VjDJ2qULaQdpjL4Z7xw0E1FiOEECWLXi5g71LWObg6Ip0ekbJAsj4JBZIZGzi0Q/1NPLdAnmPWRco4DLWPQ5KWYwKBpYhTB1EgGTVwCMk47FcEDpenSbYBAArMgdqGelaOJlH++/suL/95VmjkFtPeiVcN+94UoZQbrDEMbAc+IMc4upa6EkLSjzLj0C4qeznEV+MAhBdIMhZ5d954ZVjgIF9gERp8I5YDABg0wGV3KM4LX1Wh2iAqDlIfB0XGAQkGDoC6n8OxS9Q5CCJDJ/IgMHW9gnKqot3VJrUcBZA20xRA4O8pML/H4V1RXilxAx/oR6GHD9cr9oPwVqxM9RBVnSMBoNMpB5JW/ehb6koISS8WVeCgzDjEvyRzuk3eWqDb24ULrvMJjSnDAgf1H2ezbx3qC1bBsfJFiDZ5V87gt0TligSNYtvmSxEV0ZpqOWaCqyoAoHoQHSQFkUGEBs6QDbuUGYe/nn1XelxqHo/xlvTqFVBoCWQdlNMVtZpTeFP3LA4ZfgQbF5jOEXgjvBXXpXx8+pDPXGe/MuNAgQMhJDHKjEOXar+K+KcqbIZ8TDDLG1wlOl2R0YFDPavAB1P/JzzVt6nPGwgcmpminbHzAuB3xfV7BMWW2srlmIlOVQDqjENjhxNevxh9HAMBjBD612gOzKl7BQ92Nv8/6fC1469PeHypVmAOTD0cYxNhN5dLx5fyX8OiuPZ9k1aB6cO7hQ630MDhQIu8J4qV9p0ghCTIrMhcqno5DGKqAgCmK5ZlJlogmdGBAxDeihoI9HwAgFZWBB+Tozm+90xcv0cVOCRxOSYAVBaapW6EfpGhsTN6gWRwHP7Qv0ZN4IP2t9b30e0N7L6o1+hx46RbEh5fqgXrHAAO9XnRV0yw6bdFfW04aTUc+CgrW6200yUhJEFaDSfd25TdIwczVQEktxFU5gcOuvB/vIPfEgXwOMfkLot8b0vYuQc79uOHH6/B/zj4L/CLgYp5QTFVYUzickwA0PIaVBXH188hGDiIUf4a3zv3V+nxqvHfQZ4+fOnmaBfMOADAF+arIp7jy5sM38SlqRpSmNCsQxDtdEkISQaL1ARqaDUOgHplRaPjJFxxZtgjyfjAIdIxDcdBO7A3c4tyuqLndNi5/9H072jtP4e9Fz/Gpxc/ARA6VZHcjAMQUiAZo84hGMB0RtiN0elz4pvuI9Lz6yZ8NyljS7UCi9w18phQiqOmywEEUnb/Z/LLcCx7Dr03/0nKsowEly/ydBIFDoSQZIi0Q+Zg9qsAgPKcCpi1gQUBIhNwrOfbIY8n4wOHaLs/Bs9tZvIyP01Pc9h5ym/pR7q+AgCIivuEso9Doi2ng6rj7CAZDGB+6rtHOua8/DEAwIGLByCwQAOpHF0Oqm3Twn9AGihUZBza+rx41rIZ67yP4TrPL9BeeDncM34IcZTuA2GhwIEQkgS5xsC/g6qttX19wCDaR/Mcj2l58uqKRKYrMixwCA8SkIqyOgAAIABJREFUjLrIf0QdHx448L3hgcNl+XLXwuA3eL9qqmJ4Mw4n2vvgFyJ/ow0GDgdYDQ4t+DUcy3+J/jkPAwD2nZe30J5TeDl4Lj3n2+UaB+DQ2R7sPevF++I8tMOmqjYeScq/ryCeA4oU2RJCCBmq4C7GYftVONsH9XOUBZLf2Ie+siLDAof4iiMBeTtk9VRFpMBBnhc61XsS/X6ndCPnIcDAyZ0CkxU4TC6ygB+YSvEKDKe7Is9F+RVTJt2ly+Ce/gOpl8S+8/LeFPOKU9tNMZkKYtx8TRHqV0bCw0vKUVVkwZraUvzkmimoKDTj4SUVsI3CnUcJIeknGDh4oYNDVygdjzS9HkuyCiQzKpcaucYh8s0lWNDWoso4nAFEAdDI75lkrYBFa4XT3wcRIr61H8UYbeDiK7MNQPICB4NWg8pCs9RyuqHNgapiS9h5irhBCjQA4ILzAloccqHnvKI0DhzM0W++llGScVhUUYBFFXJb6dWzRufUCSEkPQWnKgCgXTcROb5AfQPfcwq+iUvi/jnT8+Wpil7f4JoeKmV+xiHKVEVwcyJlxoETvdA4L6jO03Aa1bak9d1fwycMtJsepsABAKbFUeegLNLkFbtdHu2SN30aaxqHMaaxSRtXqhXGyDiMlqkKQggZTrmKZnLn+VLpMW8/Fen0qHJ0uZhkLU94PJkfOESdqggc74cRbr38bTHSkkzlMpZvuo/ANzBVYeRCClO0yds8qlqxU2ZcgYMi4/Btl1wtOzWvOmljGgmxpiOiFb4SQkgmUQYOLZyc0eTtTYP+WcrpiqHKqMAhUpAQqY8DoN6cqM8g/0Vo+lrDzlUWSB61fwOvEKhrUGYcBK0R4JJ3OWsUGYfj7X2qIEH6nSxy4HCsS95vvSp3atLGNJpoNRwm2pKX4SGEkNFKGTg0iXIGebAZB0D9RXioMqrGIVIjnkhZCEDuHgkADsMYFDnqAQC8IzxwqMmbDg3HQ2QC+v39OOM8DSCwFFMEsKm4EH+zmKF/byXy9Dbk6/ORp8+HTW9Dnt4Gm94GmyEfNtWxfBhibG89pdgCDReoY3D5RJzpdqG80Kw6xx8t49AtBw5TctM74wAAK6cW44Pjgerh/351FY639WFxZQFsMeofCCEkUyhrHI4LisDBcQYQfNKuwPXne7FjXwu6+r0Yn2fEPy+bjJIc9X0mGRmHjAocQgsheQ5So6dQ6sBB/ovQ9J0LO9ekNaEqZwqO9wZuyCf76gFMhAle7DcasNsaKFz0+/vR7+/H+f7w4CMSI28aCDJsckChCDDGjW3H+W4tmGDBkQsdKC8sk97LGAuZqgj8f6e7Ax0ueYnOlDSfqgCAx5ZXojTPiKnFFlw7reTSbyCEkAySp8g4HPMUgoEDBwZO9IN3nIFgqwQAPP9hI+ovOAAA317sg0Grwb9+p0b1s8qsk5Cjy4HD5xjyeDIscFBnF4w6HhwXLXCQz+3RKZpAOcIDBwC4rGCmFDg0OQcCB86Dw8ah1zW4BRfOu1w474oSaNgAy0D/qX9rBn5z1og8vQ3FxhLcXnGX6tRgceSJ3uPSsUJDEQoMBUh3xVYDNiytuPSJhBCSgZQ77XZ4OIgFEwLZBgTqHIKBQ1Nnv+p9f2/qhsgYNIr7oIbTYO3UH+HVb18Z8ngyOnCINk0BqDMRPXrFkswINQ5AoM7hrdP/AQA43X8UwHUwwosjBjlw+F7ZLVgy5krYvXbYvXb0eO2we7ph93bLz712OP2xt8uOxi244XZdwEXXBZzoOQZO+89gfpvqz3O8RzlNkZn1DYQQkk2UUxU+gcGXWy4HDj2BAkm3T0C/T1C9z+7y4XhbH2rG5KiO3zTpVtxQdvOQx5NRgUNocWS0wkgA0Grkc7u1yozDWYAxICRTMcMmF5TYfW3gtL0wim4cNMjLBZeMuRKXFy+85Di9ghc9vh70eLth9wQDCjnACAYZF52daHN1guPdYT/DI3pgKPkr3K3fBxCocWCMYc+Fj6RzamzTLzkWQggho1tuSPt6p3USjNgLQG4CZXf5Ir73i2Z7WOAAIKFuwhkVOAwm46Cscejki6XHGp8TnLcXzKDeOKrYVIIS4xi0uS8CAHhTM5ivB3Zevvjx3qj1vB7FfDGKjcUxz+vz+LH8f30GQACndeLfbivHKdcB/O74bwN/hryv4O1aAtE9ERqOw4ne4zjlaJTev3zcyrjGQwghZPTSazUwajVw+wOtAByGsQj2jwxOr3f1RwscunHP/IlJHU9GLccMLY6M1sMBUAcOvZo8MMUKh6h1DoplmbzpNJzGDul5OfTI0eVGetuQWQ1alOWbAPBg/lz0943B9yffrVpiqbMdhEbfhq/tB/GT/Y9Lx2cXzcZEa1mEn0oIISTdKJdk2hV1ebzjLACgO0rg8NW5Hnj8kfc7GqoMCxwGk3GQX/OLDELOeOl5tDoH5fpX3tyMbpNden4ZF77RUTJUKzZQamjrA8/xuGXSbdIxff7nsEx+ET899Bi6vV3S8Rsn3zQs4yGEEJJ6eYq9bzp4xfT6wP2q2yX3FZoxNgcGrQal6MBa7ITnix2BZZtJktGBgybKigpAXRzpFxlEqxw4RM84yIGDxngOp3PkjMMsPn/Q442HcufFYwMdJJeMXQqeiz7LZDPkY9WkVcMyHkIIIamXo6hzuMgpptc9PeC8DlXGodiqxwbLh9hj+G94QvcnTD78c+S+/wgg+pEMGR04xIgbVBkHn8Ag5Cj6f0fo5QAAlTmTYeLNAz+bwcsHKlh5xrDUUBrxPYmqHqPOODDGkKPLxSzbvLBzZ+TPxJqKO/HaNa/BqhueDAghhJDUU05VXBRzwTRyYb7GcVYVOFRp27HeswNaTp6iMDTuguWzp5MylowOHGJRZhx8ggjRomw7fSHSW8BrtJgWoQByocuNfH1ehHckTjlVYXf5cNER2B9jftEy6TgTebx25e/xyhXb8fC0jajMqxyWsRBCCBkZeYolmb0eUf1l13EOXYpVFVc7d0GD8G0KTF+/CfjDV+kNVkYFDqFdImMkHFTFkT6RQTTJjZI4T/TtRucUhX/Tv9bZn9SdMZVsJh1Kc+XCzeCGV5cXLIevdwZEvxW+C7ejIoeCBUIIyVTKjEOv2x8yvX4W9oGMgxEezOnaJb32nO9OuBC4h3CiF9q2IwmPJaMCh7AukTHmKlTFkYKoWn6p8dgjvQUAcFv5HSjWyi08jaKIFf39YFpz1PckqlqxBrehLRA4cODhPnc3nCeeApx1w/a7CSGEjLwcVeDgg5AzQXrOO86hqz9QHPk9fh+MQi8AwMkM+IOwEoeEydK5uvNfJjyWjAocQsXKOKinKhiYwSa/L0bGQc8bcJX5KbgvfA/TnTyea+9ErsjA9JZkDDkiVYHkQOCg2qciVjEHIYSQtJcXmnHIURf0BxpAMdzL75aO72RL4YAZB5hiCf+FAwmPJaMDh1jUxZEiRKMcOGjc0TMOACCIgK97MX5yQYMV/S4AABvGYkRVgeTFCIFDlI28CCGEZAZl2+letz8k43AWXf0+zOFOYIamWTr+SW5gWf4BUd7sUHd+P8AS6+uQ0YFDvBkHvxiacbAH2k5H4RMCr1kgF5kw/fAFDsqMQ4fTi44+D/yMAgdCCMkWoVMVyowD5zgLj1/EP2nfk455x18BbUlgWv2QOAXiwB1R4+kB330yobFkdOAQq49DWHGkIuPAiX7A1x/pbQACgQYAWDiXdIzphm+qotCiR7FVXnpzrM1JGQdCCMkiqqkKjx+CTd4xWNvfhglcO67WHJKOuS67BxPzA0X7DphxViefn+h0RUYHDnmm6E2Swooj9ep20bEKJH1CIM1jVWUcwjcRSSZ1B0lHSI3DsP5qQgghI0w5VdHnEfDo+124qJWXZN7L/xdyB77MMk4Db9lyjM+TV/vVo0p6rG2vT2gsGRc4rLsisD8Dr+HwyJUVUc8LLY48ZffCxckrI2IVSPpEBoDBAjnjIA5jxgEApoXUOSgDh9BlqIQQQjKLcjkmAHx6qgvvu6dJzx/Q/lV67C+cDugtmGAzSscOeOSaCG3HNwmNJaN2xwSA+xdOwqzSXEywmTA21xj1PGXGof6CA3f874P41GDGBC4wRaFxd0OI8l6/IMIED3hOvnkPZ40DAFSXKJZkXuyDQDUOhBCSNSx6HjwHCIryu73iTPwQfws71z92LgBgfJ58D/yHbxIG2jlA23EUEKPd4S4taRmH119/HU888YT0fOPGjZg1axbq6upQV1eH1atXR3xfV1cX1q1bh7q6OqxatQp79uxJaBy8hsPC8gJMsMVuyKSLkN/vYXLWgIsxVeEXmWqaAhjeVRUAUKPIOFxweNDRJ29oQoEDIYRkNo7jkKOYrgCAfeIMCCz833/fQOBgM+lg1gV2jf6WlYEN3PI5vwt8T9OQx5Jw4CAIAn7729/il7/8pep4Q0MD/vSnP+HQoUM4dOgQ3nrrrYjv37JlCyZOnIgvvvgCTz31FB5//HF0dHREPDeZIqX3lYGDJtZUhcDUhZGcBtBGz24kQ4lVj3zF7mj7Tss7YeaFfJgIIYRkntDpil5Y8BWbHHZeMHDgOA7jB6Yr3DCg1zxJOkfb/vWQx5Fw4LB582YcPHgQa9askY45nU60traiqqoqxjsD53300Ud45JFHoNfrsXTpUsydOxe7d++O+b5kUE5VBNkhf6vnYvRy8ItiyFLMnNg7aiUBx3Gqfg6fNXVLjwst+khvIYQQkkFCAwcA2Oa/TfVcNBVBzC2TniunK84ap0iPte1Dr3NIuMZh06ZNKCkpwSuvvIJz5wK7Sh4/fhxmsxnr1q3DsWPHUFNTgy1btmDyZHVk1NzcjJycHBQUyPtEVFRUoKkpvhQKz2uQnz+0Vs8FA5tFKdkVGQcz1w9jtJ/NaZCjyDhwBuuQxzEYtWX5+Px0IGBweOTtUccXmlW/nx8IilIxpnRD1yY2uj7R0bWJjq5NbMm6PoU5BuC8Q3VsrzgL93g343eWX4H39QFz70V+gXwvqxqbg49PdgIATumqMAOBXg9Ge8OQx5FwxqGkpCTsmMvlwuzZs7FlyxZ88sknmDNnDtavXw+fz6c6r7+/HyaTuhbBaDTC5XJhuEXKOPQqMg5wdYe9HuQTRNWKCgxzYWTQjNLciMeLrYaIxwkhhGSOPFPkaem2kiUQH/0a/nV7IV71E9VrExXBymHfROkx1350yOMYllUVixYtwqJFi6TnGzZswBtvvIHGxkbU1MgbRJlMJrjd6iJDt9sNszm+qEwQRHR3R2/UFIvLGTvj4OvtQG+Un+32+jFOMVXh582wD3EcgzHBEvlDY9ZAdR2CUe1Qr00mo2sTG12f6OjaREfXJrZkXR9jhCnx9UvKcW1NCewuHaCvAOzqL94FevlL8me9xdJjztk+5HEMSx+Hjz76CLt2ydt6CoIAQRBgMKi/GU+aNAkOhwN2u1xP0NTUhIqK6P0XkkWnCf+j90C5qiJ2caSVU9Q4DPOKiqCxucaIRZ1FFso4EEJIpgutcZg2xor7FpShNC96cX6ZIuPwbZ8JgmJ7haEalsDB5/PhmWeeQWNjI7xeL1544QVMnTo1LCCwWq248sorsW3bNng8HuzduxcHDhzANddcMxzDUtFGWI5pZ/EVR/pEEVbIkeNw93AI0mq4iB+QwiiZCEIIIZkjN2SqIp7C+HG5BhSYg+/j0G2uTHgcwxI4rFq1CmvXrsXatWuxYMECnDhxAi+99BIAoLW1FXV1dWhtbQUAPPPMM2hvb8fixYvx9NNP48UXX0RxcXGsH58UEZdjIr7lmH6BwcKlZoOrUMpOYEG0qoIQQjJfrkGdcSiK499+juMwS1Ef18RNjHF2fJJW47BhwwbV8wceeAAPPPBA2HmlpaU4dEjeiKOwsBC//vWvkzWMuEUqjuxRZhxi7VUR0gBqODe4CjUhzwRALtzkAOSbKXAghJBMFzpVEe+XxlmludLKiq+843B5guPIuL0q4hWpc6SyOFLjdQCiH+d73Xj+byex8+vz0mv+kFUVqapxAIAJ+epVKPlmHe1VQQghWSA0cIgn4wAAM8fJGYe9PYln9DNur4p4Rco4dCJkh0xnG559vxP7BnonVBVbMWNsTqDltGKqQkzlVEVIjQNNUxBCSHYI7RIc77//NWOs0Go4+EWGb3ylAJ/YOLI34xDhW7obBrSzPOk539ssBQ0A8McDZwEMrKpQZhxSWuOgzjhEW9dLCCEks+SEZBws+vgiAKOOR3VJ4D7ViTw4dYUJjSNrA4doG0M1szHyOT3NqteCS2h9gqjeqyKFUxWhqyr8gpiy300IIWTkhE5VxNoBOpSyQPLPufdC1EduKBiPrA0cOI6LWBvQwuROmJreFtVrwWAjdHdMpk9dcaRBq/4rc/kocCCEkGyg4zV4YGEZdDyHW2aNRVl+7F2glWYqAoftjkXofKB+yOPI2hoHIFAg6ReZ6tgZReDA9zYDWCg95zgOjDH4RTZixZGhZkdpQ00IISTzPLi4HGsXlkWs04tFmXFo7fWgw+nFUMskszbjAEQukGwRFRmHkKkKnoMUaKj6OKRwOSYAPH/jdOh4DoUWPe6/ouzSbyCEEJIxBhs0AMCYHANKrHIx5ZGQzbIGI6szDpGmKppVGQf1VAXHyRkKdXFkzjCNMLJlU4qw+6ErYNJpoB3CB4gQQkj2mVWahw+OB/aoOHKuF3cM8edk9V0nco2DojjS3aVqLa3hAoWROvhh4ORtrVNZ4xCUY9RS0EAIISRuM0vlL7n1F3qH/HOy+s4TKd3Tjjy4mbzEsYxrkx77BAafoK5vAEa2xoEQQgiJR3BJJgC0dLtinBlblgcO4RkHBo2qQPI/Df+C+dy3AACPX4RfZCji5H0smEaX0j4OhBBCyFCMVyzn7+r3DfnnZHngEPmPr6xzAIDf6l9EEXrg9gnwCSLGcnJTKNEyBuCy+jISQghJA8VWQ8QvzIOV1Xe8aHs8vCtcoXpu45z4qe5NuP0i/ALDOK5Tek20jB3WMRJCCCHJwGs4lA6iaVQ0WR04hGYcgl253hGX4BdlO3A+X95D7EZ+H/TtR/CrT5swRrE7pUCBAyGEkDQx3kaBQ0JCMw42xb4PpzTl+Mv0l/GtKO9dXultwMcnOzGW65KOiVYKHAghhKSHCXnxd5uMJqsDh9C5njxFH3CXT4TTz+GQWCUdq+AuAADGqGocKHAghBCSHijjkKDQqQrlTpNunwCXV8BpJgcGk7iLAKDOOFDgQAghJE2MT0LGgTpHKoRmHPp9AtoUgUP5QMZBtaqCpioIIYSkCco4JChmxsEvwOUT0MTGScfKuDYY4EUh5I5bgmUcCCGEkHQwIY8Ch4SE1jjYQqYqnF5Btc22jhMwR3MCGk7eUVO0jAEhhBCSDow6HuNyDQn9jKwOHMKmKhSBg93lh93lgwd6nGOF0vHtum3S4x4uB9AmHr0RQgghqfLIlRUYmzP04CHLaxzUcZNNUePg8Pjx+elALcNpcSzG84GmT7mcvOlVl6YQ1GyaEEJIOllVU4JVNSWXPjGKrM44iIypniszDkrKlRVK3ZqCpI+JEEIIGc0ocFCwRQkcmqIEDi3chKSPiRBCCBnNsnqqQlDHDSixGqDnOXhDXtgnzpAenxGL0cxKoOf8eBPXY3EqBkoIIYSMElkdOIiiOkCwGng8vnwynv3gpOp4PSvHnd6nMIm7iLeFJfAikJkwMz5lYyWEEEJGA5qqUOA4Dqtnl+KjRxaFnfu5OB3/V1guBQ0A0O8Thn2MhBBCyGiS1YGDIEY+bjVoo9Y7EEIIIdksqwOH0IyDUrFVn8KREEIIIemBAocoSqyXbo7xxMqqS55DCCGEZJKsLo4UxOiBQ1GMjMNNM8eidnwurk2ggQYhhBCSjrI6cCiwRA8OJhdZor5288yxuGxc7nAMiRBCCBnVsnqq4qHF5TBoA5dg/ZJy1Ws3zxwbtc7Bqs/qeIsQQkgWy+o7YJFFj50PzEdrjxuXjctRvWbS8fj3f5qLkx1OvPP1BfzXt23Sa1YD9W8ghBCSnbI64wAAhRY9ZpbmguO4sNfyTDrMnWgDY6GNorI63iKEEJLFsj5wiIcvpAV1cHqDEEIIyTZ0B4yDXwzvMEkIIYRkIwoc4uAXo7SYJIQQQrIMBQ5x8Iduo0kIIYRkKQoc4nDzrHHS4ynF0fs7EEIIIZmOlgfEYcXUItw9bwKau1145MqKkR4OIYQQMmIocIiDhuOw8arKkR4GIYQQMuKSNlXx+uuv44knngg7vm/fPtTU1ER93/HjxzFt2jTU1dVJ/9u9e3eyhkUIIYSQJEo44yAIAl577TVs27YNN910k+q1vr4+PPnkk2ENlJQaGhqwbNkyvPrqq4kOhRBCCCHDLOGMw+bNm3Hw4EGsWbMm7LWtW7di1apVMd/f0NAQMyNBCCGEkNEj4cBh06ZN2L59O4qLi1XHP/nkE7S0tOCuu+6K+f5jx47hwIEDWL58Oa6++mps37490SERQgghZJgkPFVRUlISdqynpwdbt27Fjh07Ltll0WazYcaMGbjzzjvR2tqKBx98EMXFxVi9evUlfzfPa5Cfbx7y2DMVzwfiQbo24ejaxEbXJzq6NtHRtYkt067PsPRx+PnPf4677roLZWVllzz3hRdewNq1a2E2m1FVVYW77roLH3744XAMixBCCCEJGpblmO+99x4+/vhjvPzyyxAH2jXPmzcPv/nNbzBv3jzpPLfbjZdeegnr169HTk5gW2uv1wuDwRDX7xEEEd3d/cn/A6S5YFRL1yYcXZvY6PpER9cmOro2sY3W61NcnDOk9w1L4HDkyBHp8dmzZ7FixQocOHAg7Dyj0YhPP/0UjDE8/vjjaGpqwh/+8Ac8/fTTwzEsQgghhCRoRFpO19XVSYHEK6+8gmPHjmHBggVYt24dHnroIVx11VUjMSxCCCGEXALHYjVZGOX8fmHUpX5Gg9GaFhsN6NrERtcnOro20dG1iW20Xp+hTlXQJleEEEIIiRsFDoQQQgiJGwUOhBBCCIlbWtc4EEIIISS1KONACCGEkLhR4EAIIYSQuFHgQAghhJC4UeBACCGEkLhR4EAIIYSQuFHgQAghhJC4pWXg8NVXX+Hmm29GbW0tfvCDH6ClpWWkhzSifvGLX2DmzJmoq6tDXV0dFixYAAD4+OOPce2116K2thYPPfQQurq6RnikqfP666/jiSeekJ7HuhbZeJ1Cr8/GjRsxa9Ys6TO0evVq6bW33noLy5YtQ11dHTZt2gS32z0SQx52H3zwAb773e9izpw5WL16NQ4ePAiAPjtA9GtDn5uA3bt347rrrkNdXR1uvfVWHDp0CEDse1Va38dYmnG73Wzx4sXsL3/5C/N4POyVV15ht9xyy0gPa0Tde++97L333lMda2trY3PnzmWff/45c7vd7Mknn2SPPPLICI0wdfx+P9u+fTurqalhmzdvZozFvhbZdp0iXR/GGLvmmmtYfX192PlHjx5lCxYsYA0NDczhcLB169axZ599NpVDTomWlhY2Z84c9sUXXzBBENjOnTvZ/PnzWWtra9Z/dqJdG4fDkfWfG8YYO3PmDKutrWWHDx9mjDH25z//mS1dujTmvSrd72Npl3H4/PPPYbPZcMMNN0Cv1+Phhx/GmTNncPLkyZEe2ohpaGhATU2N6tj777+PuXPnYsGCBTAYDHj88cfx4Ycfoq+vb4RGmRqbN2/GwYMHsWbNGulYrGuRbdcp0vVxOp1obW1FVVVV2Pnvvvsurr/+elRXV8NqtWLjxo3YuXNnKoecEufPn8ftt9+O+fPnQ6PR4MYbbwQAvPPOO1n/2Yl2bY4ePZr1nxsAmDBhAj799FPMnj0bXq8XdrsdNpst5r0q3e9jaRc4NDU1obKyUnrO8zwmTpyIU6dOjeCoRk5bWxvsdju2bt2KhQsX4vbbb8fhw4dx6tQp1XXKz8+H1WpFc3PzCI52+G3atAnbt29HcXGxdCzWtci26xTp+hw/fhxmsxnr1q3DwoULce+996KxsRFA+LWrqKhAV1cX7HZ7ysc+nObPn4/NmzdLzw8fPgyXy4WWlpas/+xEuzYAsv5zE2SxWHDs2DHMnj0b27Ztw6ZNm2Leq9L9PpZ2gUN/fz+MRqPqmMlkkj7I2aa7uxvz58/Hj370I+zZswdr1qzBgw8+CKfTmZXXqaSkJOyYy+WKei1ivZaJol2f2bNnY8uWLfjkk08wZ84crF+/Hj6fL+z6mEwmAMjo+erm5mZs2LABjz76KDQaDX12FJTXxu/30+dGobKyEkeOHMHPfvYzbNiwIea/wel+H0u7wMFkMoV9+FwuFywWywiNaGRVV1fjjTfeQF1dHfR6PdasWYOSkhJ8+eWXdJ0GxPrM0OcJWLRoEXbs2IGqqioYDAZs2LABHR0daGxshNFohMfjkc5VftPMREeOHMGdd96J22+/Hffffz99dhRCrw19btR0Oh10Oh1uvfVWjB8/PqM/O2kXOFRWVuL06dPSc0EQ0NLSgoqKipEb1Ag6ePAg/vjHP6qOeb1e3HPPParr1NXVhb6+PpSVlaV4hCOvoqIi6rWI9Vq2+Oijj7Br1y7puSAIEAQBBoMBlZWVaGpqkl5rampCUVERcnNzR2Kow2rv3r2477778Nhjj2HDhg0A6LMTFOna0Ocm4LPPPsN9992nOub1emPeq9L9PpZ2gcOCBQvQ2dmJd955B16vF6+++irKysowefLkkR7aiNDr9Xj++edx4MAB+P1+vPnmm/B6vbj22muxf/9+/P3vf4fH48G2bduwfPnytIlok2nlypVRr0Ws17KFz+fDM888g8bGRni9XrzwwguYOnUqKioqcP311+Pdd99FfX09+vr68PLLL+OGG24Y6SEn3enTp7Fx40Zs3bpVVThKn53o14Y+NwE1NTWor6/Hrl274Pf78fvf/x6CIGDhwoVR71WjeSQVAAABX0lEQVRpfx8b6WUdQ/H111+z1atXs9raWvb973+fNTc3j/SQRtTbb7/NVq5cyWbPns3uuOMO1tDQwBhjbM+ePew73/kOq6urY+vWrWOdnZ0jPNLUefnll1XLDWNdi2y8TqHXZ8eOHWzp0qWstraW3X///ay1tVV67e2332YrVqxgc+bMYT/+8Y+Zy+UaiSEPq+eee45VV1ez2tpa1f/279+f9Z+dWNcm2z83Qfv372c33XQTmzt3Lrv77rtZY2MjYyz2vSqd72McY4yNdPBCCCGEkPSQdlMVhBBCCBk5FDgQQgghJG4UOBBCCCEkbhQ4EEIIISRuFDgQQgghJG4UOBBCCCEkbhQ4EEIIISRuFDgQQgghJG4UOBBCCCEkbhQ4EEIIISRuFDgQQgghJG4UOBBCCCEkbhQ4EEIIISRuFDgQQgghJG4UOBBCCCEkbhQ4EEIIISRuFDgQQgghJG7/H9sMEl54/F+wAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 600x450 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data['收盘价(元)'].plot()\n","data['收盘价(元)'].rolling(5).mean().plot()\n","data['收盘价(元)'].rolling(20).mean().plot()"]},{"cell_type":"markdown","metadata":{},"source":["### 73.按周为采样规则，取一周收盘价最大值"]},{"cell_type":"code","execution_count":97,"metadata":{},"outputs":[{"data":{"text/plain":["日期\n","2016-01-10    15.9855\n","2016-01-17    15.8265\n","2016-01-24    15.6940\n","2016-01-31    15.0405\n","2016-02-07    16.2328\n","               ...   \n","2017-04-16    15.9700\n","2017-04-23    15.5600\n","2017-04-30    15.2100\n","2017-05-07    15.1600\n","2017-05-14    14.8600\n","Freq: W-SUN, Name: 收盘价(元), Length: 71, dtype: float64"]},"execution_count":97,"metadata":{},"output_type":"execute_result"}],"source":["data['收盘价(元)'].resample('W').max()"]},{"cell_type":"markdown","metadata":{},"source":["### 74.绘制重采样数据与原始数据"]},{"cell_type":"code","execution_count":98,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12c56c6d0>"]},"execution_count":98,"metadata":{},"output_type":"execute_result"},{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26085 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26399 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26085 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26399 missing from current font.\n","  font.set_text(s, 0, 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\n","text/plain":["<Figure size 600x450 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data['收盘价(元)'].plot()\n","data['收盘价(元)'].resample('7D').max().plot()"]},{"cell_type":"markdown","metadata":{},"source":["### 75.将数据往后移动5天"]},{"cell_type":"code","execution_count":99,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>index</th>\n","      <th>代码</th>\n","      <th>简称</th>\n","      <th>前收盘价(元)</th>\n","      <th>开盘价(元)</th>\n","      <th>最高价(元)</th>\n","      <th>最低价(元)</th>\n","      <th>收盘价(元)</th>\n","      <th>成交量(股)</th>\n","      <th>成交金额(元)</th>\n","      <th>涨跌(元)</th>\n","      <th>涨跌幅(%)</th>\n","      <th>均价(元)</th>\n","      <th>换手率(%)</th>\n","      <th>A股流通市值(元)</th>\n","      <th>总市值(元)</th>\n","      <th>A股流通股本(股)</th>\n","      <th>市盈率</th>\n","    </tr>\n","    <tr>\n","      <th>日期</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2016-01-04</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2016-01-05</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2016-01-06</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2016-01-07</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2016-01-08</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-03</th>\n","      <td>317.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.00</td>\n","      <td>15.02</td>\n","      <td>15.10</td>\n","      <td>14.99</td>\n","      <td>15.05</td>\n","      <td>12975919</td>\n","      <td>195296862</td>\n","      <td>0.05</td>\n","      <td>0.3333</td>\n","      <td>15.0507</td>\n","      <td>0.06</td>\n","      <td>3.253551e+11</td>\n","      <td>3.253551e+11</td>\n","      <td>2.161828e+10</td>\n","      <td>6.1273</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-04</th>\n","      <td>318.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.05</td>\n","      <td>15.06</td>\n","      <td>15.11</td>\n","      <td>15.00</td>\n","      <td>15.05</td>\n","      <td>14939871</td>\n","      <td>225022668</td>\n","      <td>0.00</td>\n","      <td>0.0000</td>\n","      <td>15.0619</td>\n","      <td>0.0691</td>\n","      <td>3.253551e+11</td>\n","      <td>3.253551e+11</td>\n","      <td>2.161828e+10</td>\n","      <td>6.1273</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-05</th>\n","      <td>319.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.05</td>\n","      <td>15.05</td>\n","      <td>15.25</td>\n","      <td>15.03</td>\n","      <td>15.21</td>\n","      <td>22887645</td>\n","      <td>345791526</td>\n","      <td>0.16</td>\n","      <td>1.0631</td>\n","      <td>15.1082</td>\n","      <td>0.1059</td>\n","      <td>3.288140e+11</td>\n","      <td>3.288140e+11</td>\n","      <td>2.161828e+10</td>\n","      <td>6.1925</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-08</th>\n","      <td>320.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.21</td>\n","      <td>15.15</td>\n","      <td>15.22</td>\n","      <td>15.08</td>\n","      <td>15.21</td>\n","      <td>15718509</td>\n","      <td>238419161</td>\n","      <td>0.00</td>\n","      <td>0.0000</td>\n","      <td>15.1681</td>\n","      <td>0.0727</td>\n","      <td>3.288140e+11</td>\n","      <td>3.288140e+11</td>\n","      <td>2.161828e+10</td>\n","      <td>6.1925</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-09</th>\n","      <td>321.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.21</td>\n","      <td>15.21</td>\n","      <td>15.22</td>\n","      <td>15.13</td>\n","      <td>15.16</td>\n","      <td>12607509</td>\n","      <td>191225527</td>\n","      <td>-0.05</td>\n","      <td>-0.3287</td>\n","      <td>15.1676</td>\n","      <td>0.0583</td>\n","      <td>3.277331e+11</td>\n","      <td>3.277331e+11</td>\n","      <td>2.161828e+10</td>\n","      <td>6.1721</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>309 rows × 18 columns</p>\n","</div>"],"text/plain":["            index         代码    简称  前收盘价(元)  开盘价(元)  最高价(元)  最低价(元)  收盘价(元)  \\\n","日期                                                                            \n","2016-01-04    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n","2016-01-05    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n","2016-01-06    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n","2016-01-07    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n","2016-01-08    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n","...           ...        ...   ...      ...     ...     ...     ...     ...   \n","2017-05-03  317.0  600000.SH  浦发银行    15.00   15.02   15.10   14.99   15.05   \n","2017-05-04  318.0  600000.SH  浦发银行    15.05   15.06   15.11   15.00   15.05   \n","2017-05-05  319.0  600000.SH  浦发银行    15.05   15.05   15.25   15.03   15.21   \n","2017-05-08  320.0  600000.SH  浦发银行    15.21   15.15   15.22   15.08   15.21   \n","2017-05-09  321.0  600000.SH  浦发银行    15.21   15.21   15.22   15.13   15.16   \n","\n","              成交量(股)    成交金额(元)  涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n","日期                                                                              \n","2016-01-04       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n","2016-01-05       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n","2016-01-06       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n","2016-01-07       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n","2016-01-08       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n","...              ...        ...    ...     ...      ...     ...           ...   \n","2017-05-03  12975919  195296862   0.05  0.3333  15.0507    0.06  3.253551e+11   \n","2017-05-04  14939871  225022668   0.00  0.0000  15.0619  0.0691  3.253551e+11   \n","2017-05-05  22887645  345791526   0.16  1.0631  15.1082  0.1059  3.288140e+11   \n","2017-05-08  15718509  238419161   0.00  0.0000  15.1681  0.0727  3.288140e+11   \n","2017-05-09  12607509  191225527  -0.05 -0.3287  15.1676  0.0583  3.277331e+11   \n","\n","                  总市值(元)     A股流通股本(股)     市盈率  \n","日期                                              \n","2016-01-04           NaN           NaN     NaN  \n","2016-01-05           NaN           NaN     NaN  \n","2016-01-06           NaN           NaN     NaN  \n","2016-01-07           NaN           NaN     NaN  \n","2016-01-08           NaN           NaN     NaN  \n","...                  ...           ...     ...  \n","2017-05-03  3.253551e+11  2.161828e+10  6.1273  \n","2017-05-04  3.253551e+11  2.161828e+10  6.1273  \n","2017-05-05  3.288140e+11  2.161828e+10  6.1925  \n","2017-05-08  3.288140e+11  2.161828e+10  6.1925  \n","2017-05-09  3.277331e+11  2.161828e+10  6.1721  \n","\n","[309 rows x 18 columns]"]},"execution_count":99,"metadata":{},"output_type":"execute_result"}],"source":["data.shift(5)"]},{"cell_type":"markdown","metadata":{},"source":["### 76.将数据向前移动5天"]},{"cell_type":"code","execution_count":100,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>index</th>\n","      <th>代码</th>\n","      <th>简称</th>\n","      <th>前收盘价(元)</th>\n","      <th>开盘价(元)</th>\n","      <th>最高价(元)</th>\n","      <th>最低价(元)</th>\n","      <th>收盘价(元)</th>\n","      <th>成交量(股)</th>\n","      <th>成交金额(元)</th>\n","      <th>涨跌(元)</th>\n","      <th>涨跌幅(%)</th>\n","      <th>均价(元)</th>\n","      <th>换手率(%)</th>\n","      <th>A股流通市值(元)</th>\n","      <th>总市值(元)</th>\n","      <th>A股流通股本(股)</th>\n","      <th>市盈率</th>\n","    </tr>\n","    <tr>\n","      <th>日期</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2016-01-04</th>\n","      <td>5.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.4467</td>\n","      <td>15.1994</td>\n","      <td>15.4114</td>\n","      <td>14.9786</td>\n","      <td>15.0581</td>\n","      <td>90177135</td>\n","      <td>1550155933</td>\n","      <td>-0.3886</td>\n","      <td>-2.5157</td>\n","      <td>17.1901</td>\n","      <td>0.4834</td>\n","      <td>3.180417e+11</td>\n","      <td>3.180417e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.2849</td>\n","    </tr>\n","    <tr>\n","      <th>2016-01-05</th>\n","      <td>6.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.0581</td>\n","      <td>15.1641</td>\n","      <td>15.4732</td>\n","      <td>15.0846</td>\n","      <td>15.4114</td>\n","      <td>55374454</td>\n","      <td>964061502</td>\n","      <td>0.3533</td>\n","      <td>2.3460</td>\n","      <td>17.4099</td>\n","      <td>0.2969</td>\n","      <td>3.255031e+11</td>\n","      <td>3.255031e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.4324</td>\n","    </tr>\n","    <tr>\n","      <th>2016-01-06</th>\n","      <td>7.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.4114</td>\n","      <td>15.5174</td>\n","      <td>15.8088</td>\n","      <td>15.3231</td>\n","      <td>15.3584</td>\n","      <td>47869312</td>\n","      <td>843717365</td>\n","      <td>-0.0530</td>\n","      <td>-0.3438</td>\n","      <td>17.6254</td>\n","      <td>0.2566</td>\n","      <td>3.243839e+11</td>\n","      <td>3.243839e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.4102</td>\n","    </tr>\n","    <tr>\n","      <th>2016-01-07</th>\n","      <td>8.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.3584</td>\n","      <td>15.0140</td>\n","      <td>15.8883</td>\n","      <td>14.9168</td>\n","      <td>15.8265</td>\n","      <td>54838833</td>\n","      <td>966117848</td>\n","      <td>0.4681</td>\n","      <td>3.0477</td>\n","      <td>17.6174</td>\n","      <td>0.294</td>\n","      <td>3.342702e+11</td>\n","      <td>3.342702e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.6056</td>\n","    </tr>\n","    <tr>\n","      <th>2016-01-08</th>\n","      <td>9.0</td>\n","      <td>600000.SH</td>\n","      <td>浦发银行</td>\n","      <td>15.8265</td>\n","      <td>15.7205</td>\n","      <td>16.0296</td>\n","      <td>15.4732</td>\n","      <td>15.5262</td>\n","      <td>46723139</td>\n","      <td>836146426</td>\n","      <td>-0.3003</td>\n","      <td>-1.8973</td>\n","      <td>17.8958</td>\n","      <td>0.2505</td>\n","      <td>3.279280e+11</td>\n","      <td>3.279280e+11</td>\n","      <td>1.865347e+10</td>\n","      <td>6.4803</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-03</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-04</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-05</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-08</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-09</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>309 rows × 18 columns</p>\n","</div>"],"text/plain":["            index         代码    简称  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)  \\\n","日期                                                                       \n","2016-01-04    5.0  600000.SH  浦发银行  15.4467  15.1994  15.4114  14.9786   \n","2016-01-05    6.0  600000.SH  浦发银行  15.0581  15.1641  15.4732  15.0846   \n","2016-01-06    7.0  600000.SH  浦发银行  15.4114  15.5174  15.8088  15.3231   \n","2016-01-07    8.0  600000.SH  浦发银行  15.3584  15.0140  15.8883  14.9168   \n","2016-01-08    9.0  600000.SH  浦发银行  15.8265  15.7205  16.0296  15.4732   \n","...           ...        ...   ...      ...      ...      ...      ...   \n","2017-05-03    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n","2017-05-04    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n","2017-05-05    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n","2017-05-08    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n","2017-05-09    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n","\n","             收盘价(元)    成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)  \\\n","日期                                                                           \n","2016-01-04  15.0581  90177135  1550155933 -0.3886 -2.5157  17.1901  0.4834   \n","2016-01-05  15.4114  55374454   964061502  0.3533  2.3460  17.4099  0.2969   \n","2016-01-06  15.3584  47869312   843717365 -0.0530 -0.3438  17.6254  0.2566   \n","2016-01-07  15.8265  54838833   966117848  0.4681  3.0477  17.6174   0.294   \n","2016-01-08  15.5262  46723139   836146426 -0.3003 -1.8973  17.8958  0.2505   \n","...             ...       ...         ...     ...     ...      ...     ...   \n","2017-05-03      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n","2017-05-04      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n","2017-05-05      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n","2017-05-08      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n","2017-05-09      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n","\n","               A股流通市值(元)        总市值(元)     A股流通股本(股)     市盈率  \n","日期                                                            \n","2016-01-04  3.180417e+11  3.180417e+11  1.865347e+10  6.2849  \n","2016-01-05  3.255031e+11  3.255031e+11  1.865347e+10  6.4324  \n","2016-01-06  3.243839e+11  3.243839e+11  1.865347e+10  6.4102  \n","2016-01-07  3.342702e+11  3.342702e+11  1.865347e+10  6.6056  \n","2016-01-08  3.279280e+11  3.279280e+11  1.865347e+10  6.4803  \n","...                  ...           ...           ...     ...  \n","2017-05-03           NaN           NaN           NaN     NaN  \n","2017-05-04           NaN           NaN           NaN     NaN  \n","2017-05-05           NaN           NaN           NaN     NaN  \n","2017-05-08           NaN           NaN           NaN     NaN  \n","2017-05-09           NaN           NaN           NaN     NaN  \n","\n","[309 rows x 18 columns]"]},"execution_count":100,"metadata":{},"output_type":"execute_result"}],"source":["data.shift(-5)"]},{"cell_type":"markdown","metadata":{},"source":["### 77.使用expending函数计算开盘价的移动窗口均值"]},{"cell_type":"code","execution_count":101,"metadata":{},"outputs":[{"data":{"text/plain":["日期\n","2016-01-04    16.144400\n","2016-01-05    15.804400\n","2016-01-06    15.805867\n","2016-01-07    15.784525\n","2016-01-08    15.761120\n","                ...    \n","2017-05-03    16.041489\n","2017-05-04    16.038314\n","2017-05-05    16.034769\n","2017-05-08    16.030695\n","2017-05-09    16.026356\n","Name: 开盘价(元), Length: 309, dtype: float64"]},"execution_count":101,"metadata":{},"output_type":"execute_result"}],"source":["data['开盘价(元)'].expanding(min_periods=1).mean()"]},{"cell_type":"markdown","metadata":{},"source":["### 78.绘制上一题的移动均值与原始数据折线图"]},{"cell_type":"code","execution_count":103,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12c789dd0>"]},"execution_count":103,"metadata":{},"output_type":"execute_result"},{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26085 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26399 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26085 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26399 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 24320 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 30424 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20215 missing from current font.\n","  font.set_text(s, 0.0, 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size 2400x900 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data['expanding Open mean']=data['开盘价(元)'].expanding(min_periods=1).mean()\n","data[['开盘价(元)', 'expanding Open mean']].plot(figsize=(16, 6))"]},{"cell_type":"markdown","metadata":{},"source":["### 79.计算布林指标"]},{"cell_type":"code","execution_count":104,"metadata":{},"outputs":[],"source":["data['former 30 days rolling Close mean']=data['收盘价(元)'].rolling(20).mean()\n","data['upper bound']=data['former 30 days rolling Close mean']+2*data['收盘价(元)'].rolling(20).std()#在这里我们取20天内的标准差\n","data['lower bound']=data['former 30 days rolling Close mean']-2*data['收盘价(元)'].rolling(20).std()"]},{"cell_type":"markdown","metadata":{},"source":["### 80.计算布林线并绘制"]},{"cell_type":"code","execution_count":105,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x12c7e90d0>"]},"execution_count":105,"metadata":{},"output_type":"execute_result"},{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26085 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26399 missing from current font.\n","  font.set_text(s, 0.0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26085 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26399 missing from current font.\n","  font.set_text(s, 0, flags=flags)\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: 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size 2400x900 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data[['收盘价(元)', 'former 30 days rolling Close mean','upper bound','lower bound' ]].plot(figsize=(16, 6))"]},{"cell_type":"markdown","metadata":{},"source":["## 第四期 当Pandas遇上NumPy"]},{"cell_type":"markdown","metadata":{},"source":["### 81.导入并查看pandas与numpy版本"]},{"cell_type":"code","execution_count":106,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["1.17.2\n","0.25.3\n"]}],"source":["import pandas as pd\n","import numpy as np\n","print(np.__version__)\n","print(pd.__version__)"]},{"cell_type":"markdown","metadata":{},"source":["### 82.从NumPy数组创建DataFrame"]},{"cell_type":"code","execution_count":109,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>79</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>41</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>65</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>38</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>50</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>35</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>83</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>43</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>55</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>58</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>45</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>27</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>22</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>48</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>42</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     0\n","0   79\n","1   41\n","2   65\n","3   44\n","4    6\n","5   38\n","6   50\n","7   35\n","8   83\n","9   43\n","10  55\n","11  14\n","12  58\n","13  13\n","14  45\n","15  27\n","16  17\n","17  22\n","18  48\n","19  42"]},"execution_count":109,"metadata":{},"output_type":"execute_result"}],"source":["#备注 使用numpy生成20个0-100随机数\n","tem = np.random.randint(1,100,20)\n","df1 = pd.DataFrame(tem)\n","df1"]},{"cell_type":"markdown","metadata":{},"source":["### 83.从NumPy数组创建DataFrame"]},{"cell_type":"code","execution_count":110,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>15</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>25</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>30</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>35</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>40</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>45</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>50</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>55</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>65</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>70</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>75</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>80</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>85</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>90</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>95</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     0\n","0    0\n","1    5\n","2   10\n","3   15\n","4   20\n","5   25\n","6   30\n","7   35\n","8   40\n","9   45\n","10  50\n","11  55\n","12  60\n","13  65\n","14  70\n","15  75\n","16  80\n","17  85\n","18  90\n","19  95"]},"execution_count":110,"metadata":{},"output_type":"execute_result"}],"source":["#备注 使用numpy生成20个0-100固定步长的数\n","tem = np.arange(0,100,5)\n","df2 = pd.DataFrame(tem)\n","df2"]},{"cell_type":"markdown","metadata":{},"source":["### 84.从NumPy数组创建DataFrame"]},{"cell_type":"code","execution_count":112,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.423127</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.558049</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.148458</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.435886</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.787797</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.815108</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>-1.307238</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>-1.348173</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1.037712</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2.264961</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>-0.677234</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.769742</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.457066</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>-1.092675</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>1.794632</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>1.400788</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>-0.120423</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>-1.226692</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>-0.823945</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>0.368299</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           0\n","0   1.423127\n","1   1.558049\n","2   0.148458\n","3   1.435886\n","4   1.787797\n","5   0.815108\n","6  -1.307238\n","7  -1.348173\n","8   1.037712\n","9   2.264961\n","10 -0.677234\n","11  0.769742\n","12  0.457066\n","13 -1.092675\n","14  1.794632\n","15  1.400788\n","16 -0.120423\n","17 -1.226692\n","18 -0.823945\n","19  0.368299"]},"execution_count":112,"metadata":{},"output_type":"execute_result"}],"source":["#备注 使用numpy生成20个指定分布(如标准正态分布)的数\n","tem = np.random.normal(0, 1, 20)\n","df3 = pd.DataFrame(tem)\n","df3"]},{"cell_type":"markdown","metadata":{},"source":["### 85.将df1，df2，df3按照行合并为新DataFrame"]},{"cell_type":"code","execution_count":113,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>79.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>41.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>65.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>44.000000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>6.000000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>38.000000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>50.000000</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>35.000000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>83.000000</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>43.000000</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>55.000000</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>14.000000</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>58.000000</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>13.000000</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>45.000000</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>27.000000</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>17.000000</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>22.000000</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>48.000000</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>42.000000</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>5.000000</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>10.000000</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>15.000000</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>20.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>25.000000</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>30.000000</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>35.000000</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>40.000000</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>45.000000</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>50.000000</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>55.000000</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>60.000000</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>65.000000</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>70.000000</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>75.000000</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>80.000000</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>85.000000</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>90.000000</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>95.000000</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>1.423127</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>1.558049</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>0.148458</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>1.435886</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>1.787797</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>0.815108</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>-1.307238</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>-1.348173</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>1.037712</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>2.264961</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>-0.677234</td>\n","    </tr>\n","    <tr>\n","      <th>51</th>\n","      <td>0.769742</td>\n","    </tr>\n","    <tr>\n","      <th>52</th>\n","      <td>0.457066</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>-1.092675</td>\n","    </tr>\n","    <tr>\n","      <th>54</th>\n","      <td>1.794632</td>\n","    </tr>\n","    <tr>\n","      <th>55</th>\n","      <td>1.400788</td>\n","    </tr>\n","    <tr>\n","      <th>56</th>\n","      <td>-0.120423</td>\n","    </tr>\n","    <tr>\n","      <th>57</th>\n","      <td>-1.226692</td>\n","    </tr>\n","    <tr>\n","      <th>58</th>\n","      <td>-0.823945</td>\n","    </tr>\n","    <tr>\n","      <th>59</th>\n","      <td>0.368299</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            0\n","0   79.000000\n","1   41.000000\n","2   65.000000\n","3   44.000000\n","4    6.000000\n","5   38.000000\n","6   50.000000\n","7   35.000000\n","8   83.000000\n","9   43.000000\n","10  55.000000\n","11  14.000000\n","12  58.000000\n","13  13.000000\n","14  45.000000\n","15  27.000000\n","16  17.000000\n","17  22.000000\n","18  48.000000\n","19  42.000000\n","20   0.000000\n","21   5.000000\n","22  10.000000\n","23  15.000000\n","24  20.000000\n","25  25.000000\n","26  30.000000\n","27  35.000000\n","28  40.000000\n","29  45.000000\n","30  50.000000\n","31  55.000000\n","32  60.000000\n","33  65.000000\n","34  70.000000\n","35  75.000000\n","36  80.000000\n","37  85.000000\n","38  90.000000\n","39  95.000000\n","40   1.423127\n","41   1.558049\n","42   0.148458\n","43   1.435886\n","44   1.787797\n","45   0.815108\n","46  -1.307238\n","47  -1.348173\n","48   1.037712\n","49   2.264961\n","50  -0.677234\n","51   0.769742\n","52   0.457066\n","53  -1.092675\n","54   1.794632\n","55   1.400788\n","56  -0.120423\n","57  -1.226692\n","58  -0.823945\n","59   0.368299"]},"execution_count":113,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.concat([df1,df2,df3],axis=0,ignore_index=True)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 86.将df1，df2，df3按照列合并为新DataFrame"]},{"cell_type":"code","execution_count":114,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>79</td>\n","      <td>0</td>\n","      <td>1.423127</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>41</td>\n","      <td>5</td>\n","      <td>1.558049</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>65</td>\n","      <td>10</td>\n","      <td>0.148458</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>44</td>\n","      <td>15</td>\n","      <td>1.435886</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>6</td>\n","      <td>20</td>\n","      <td>1.787797</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>38</td>\n","      <td>25</td>\n","      <td>0.815108</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>50</td>\n","      <td>30</td>\n","      <td>-1.307238</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>35</td>\n","      <td>35</td>\n","      <td>-1.348173</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>83</td>\n","      <td>40</td>\n","      <td>1.037712</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>43</td>\n","      <td>45</td>\n","      <td>2.264961</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>55</td>\n","      <td>50</td>\n","      <td>-0.677234</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>14</td>\n","      <td>55</td>\n","      <td>0.769742</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>58</td>\n","      <td>60</td>\n","      <td>0.457066</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>13</td>\n","      <td>65</td>\n","      <td>-1.092675</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>45</td>\n","      <td>70</td>\n","      <td>1.794632</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>27</td>\n","      <td>75</td>\n","      <td>1.400788</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>17</td>\n","      <td>80</td>\n","      <td>-0.120423</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>22</td>\n","      <td>85</td>\n","      <td>-1.226692</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>48</td>\n","      <td>90</td>\n","      <td>-0.823945</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>42</td>\n","      <td>95</td>\n","      <td>0.368299</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     0   1         2\n","0   79   0  1.423127\n","1   41   5  1.558049\n","2   65  10  0.148458\n","3   44  15  1.435886\n","4    6  20  1.787797\n","5   38  25  0.815108\n","6   50  30 -1.307238\n","7   35  35 -1.348173\n","8   83  40  1.037712\n","9   43  45  2.264961\n","10  55  50 -0.677234\n","11  14  55  0.769742\n","12  58  60  0.457066\n","13  13  65 -1.092675\n","14  45  70  1.794632\n","15  27  75  1.400788\n","16  17  80 -0.120423\n","17  22  85 -1.226692\n","18  48  90 -0.823945\n","19  42  95  0.368299"]},"execution_count":114,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.concat([df1,df2,df3],axis=1,ignore_index=True)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 87.查看df所有数据的最小值、25%分位数、中位数、75%分位数、最大值"]},{"cell_type":"code","execution_count":115,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[-1.34817283  1.41754194 23.5        50.         95.        ]\n"]}],"source":["print(np.percentile(df, q=[0, 25, 50, 75, 100]))"]},{"cell_type":"markdown","metadata":{},"source":["### 88.修改列名为col1,col2,col3"]},{"cell_type":"code","execution_count":116,"metadata":{},"outputs":[],"source":["df.columns = ['col1','col2','col3']"]},{"cell_type":"markdown","metadata":{},"source":["### 89.提取第一列中不在第二列出现的数字"]},{"cell_type":"code","execution_count":117,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["0     79\n","1     41\n","3     44\n","4      6\n","5     38\n","8     83\n","9     43\n","11    14\n","12    58\n","13    13\n","15    27\n","16    17\n","17    22\n","18    48\n","19    42\n","Name: col1, dtype: int64"]},"execution_count":117,"metadata":{},"output_type":"execute_result"}],"source":["df['col1'][~df['col1'].isin(df['col2'])]"]},{"cell_type":"markdown","metadata":{},"source":["### 90.提取第一列和第二列出现频率最高的三个数字"]},{"cell_type":"code","execution_count":118,"metadata":{},"outputs":[{"data":{"text/plain":["Int64Index([65, 55, 50], dtype='int64')"]},"execution_count":118,"metadata":{},"output_type":"execute_result"}],"source":["temp = df['col1'].append(df['col2'])\n","temp.value_counts().index[:3]"]},{"cell_type":"markdown","metadata":{},"source":["### 91.提取第一列中可以整除5的数字位置"]},{"cell_type":"code","execution_count":119,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:61: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n","  return bound(*args, **kwds)\n"]},{"data":{"text/plain":["array([[ 2],\n","       [ 6],\n","       [ 7],\n","       [10],\n","       [14]])"]},"execution_count":119,"metadata":{},"output_type":"execute_result"}],"source":["np.argwhere(df['col1'] % 5==0)"]},{"cell_type":"markdown","metadata":{},"source":["### 92.计算第一列数字前一个与后一个的差值"]},{"cell_type":"code","execution_count":120,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["[nan,\n"," -38.0,\n"," 24.0,\n"," -21.0,\n"," -38.0,\n"," 32.0,\n"," 12.0,\n"," -15.0,\n"," 48.0,\n"," -40.0,\n"," 12.0,\n"," -41.0,\n"," 44.0,\n"," -45.0,\n"," 32.0,\n"," -18.0,\n"," -10.0,\n"," 5.0,\n"," 26.0,\n"," -6.0]"]},"execution_count":120,"metadata":{},"output_type":"execute_result"}],"source":["df['col1'].diff().tolist()"]},{"cell_type":"markdown","metadata":{},"source":["### 93.将col1,col2,clo3三列顺序颠倒"]},{"cell_type":"code","execution_count":121,"metadata":{"scrolled":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n","  \"\"\"Entry point for launching an IPython kernel.\n","/Users/anaconda/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:822: FutureWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n","  retval = getattr(retval, self.name)._getitem_axis(key, axis=i)\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>col3</th>\n","      <th>col2</th>\n","      <th>col1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.423127</td>\n","      <td>0</td>\n","      <td>79</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.558049</td>\n","      <td>5</td>\n","      <td>41</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.148458</td>\n","      <td>10</td>\n","      <td>65</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.435886</td>\n","      <td>15</td>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.787797</td>\n","      <td>20</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.815108</td>\n","      <td>25</td>\n","      <td>38</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>-1.307238</td>\n","      <td>30</td>\n","      <td>50</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>-1.348173</td>\n","      <td>35</td>\n","      <td>35</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1.037712</td>\n","      <td>40</td>\n","      <td>83</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2.264961</td>\n","      <td>45</td>\n","      <td>43</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>-0.677234</td>\n","      <td>50</td>\n","      <td>55</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.769742</td>\n","      <td>55</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.457066</td>\n","      <td>60</td>\n","      <td>58</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>-1.092675</td>\n","      <td>65</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>1.794632</td>\n","      <td>70</td>\n","      <td>45</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>1.400788</td>\n","      <td>75</td>\n","      <td>27</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>-0.120423</td>\n","      <td>80</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>-1.226692</td>\n","      <td>85</td>\n","      <td>22</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>-0.823945</td>\n","      <td>90</td>\n","      <td>48</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>0.368299</td>\n","      <td>95</td>\n","      <td>42</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        col3  col2  col1\n","0   1.423127     0    79\n","1   1.558049     5    41\n","2   0.148458    10    65\n","3   1.435886    15    44\n","4   1.787797    20     6\n","5   0.815108    25    38\n","6  -1.307238    30    50\n","7  -1.348173    35    35\n","8   1.037712    40    83\n","9   2.264961    45    43\n","10 -0.677234    50    55\n","11  0.769742    55    14\n","12  0.457066    60    58\n","13 -1.092675    65    13\n","14  1.794632    70    45\n","15  1.400788    75    27\n","16 -0.120423    80    17\n","17 -1.226692    85    22\n","18 -0.823945    90    48\n","19  0.368299    95    42"]},"execution_count":121,"metadata":{},"output_type":"execute_result"}],"source":["df.ix[:, ::-1]"]},{"cell_type":"markdown","metadata":{},"source":["### 94.提取第一列位置在1,10,15的数字"]},{"cell_type":"code","execution_count":122,"metadata":{},"outputs":[{"data":{"text/plain":["1     41\n","10    55\n","15    27\n","Name: col1, dtype: int64"]},"execution_count":122,"metadata":{},"output_type":"execute_result"}],"source":["df['col1'].take([1,10,15])\n","# 等价于\n","df.iloc[[1,10,15],0]"]},{"cell_type":"markdown","metadata":{},"source":["### 95.查找第一列的局部最大值位置"]},{"cell_type":"code","execution_count":123,"metadata":{},"outputs":[{"data":{"text/plain":["array([ 2,  6,  8, 10, 12, 14, 18])"]},"execution_count":123,"metadata":{},"output_type":"execute_result"}],"source":["#备注 即比它前一个与后一个数字的都大的数字\n","tem = np.diff(np.sign(np.diff(df['col1'])))\n","np.where(tem == -2)[0] + 1"]},{"cell_type":"markdown","metadata":{},"source":["### 96.按行计算df的每一行均值"]},{"cell_type":"code","execution_count":124,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["0     26.807709\n","1     15.852683\n","2     25.049486\n","3     20.145295\n","4      9.262599\n","5     21.271703\n","6     26.230921\n","7     22.883942\n","8     41.345904\n","9     30.088320\n","10    34.774255\n","11    23.256581\n","12    39.485689\n","13    25.635775\n","14    38.931544\n","15    34.466929\n","16    32.293192\n","17    35.257769\n","18    45.725352\n","19    45.789433\n","dtype: float64"]},"execution_count":124,"metadata":{},"output_type":"execute_result"}],"source":["df[['col1','col2','col3']].mean(axis=1)"]},{"cell_type":"markdown","metadata":{},"source":["### 97.对第二列计算移动平均值"]},{"cell_type":"code","execution_count":125,"metadata":{},"outputs":[{"data":{"text/plain":["array([ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65.,\n","       70., 75., 80., 85., 90.])"]},"execution_count":125,"metadata":{},"output_type":"execute_result"}],"source":["#备注 每次移动三个位置，不可以使用自定义函数\n","\n","np.convolve(df['col2'], np.ones(3)/3, mode='valid')"]},{"cell_type":"markdown","metadata":{},"source":["### 98.将数据按照第三列值的大小升序排列"]},{"cell_type":"code","execution_count":126,"metadata":{},"outputs":[],"source":["df.sort_values(\"col3\",inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["### 99.将第一列大于50的数字修改为'高'"]},{"cell_type":"code","execution_count":127,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/anaconda/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \"\"\"Entry point for launching an IPython kernel.\n"]}],"source":["df.col1[df['col1'] > 50]= '高'"]},{"cell_type":"markdown","metadata":{},"source":["### 100.计算第二列与第三列之间的欧式距离"]},{"cell_type":"code","execution_count":131,"metadata":{"scrolled":true},"outputs":[{"data":{"text/plain":["247.80470977803168"]},"execution_count":131,"metadata":{},"output_type":"execute_result"}],"source":["np.linalg.norm(df['col2']-df['col3'])"]},{"cell_type":"markdown","metadata":{},"source":["## 第五期 一些补充"]},{"cell_type":"markdown","metadata":{},"source":["### 101.从CSV文件中读取指定数据"]},{"cell_type":"code","execution_count":133,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>positionName</th>\n","      <th>salary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>数据分析</td>\n","      <td>37500</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>数据建模</td>\n","      <td>15000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>数据分析</td>\n","      <td>3500</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>数据分析</td>\n","      <td>45000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>数据分析</td>\n","      <td>30000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>数据分析</td>\n","      <td>50000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>数据分析</td>\n","      <td>30000</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>数据建模工程师</td>\n","      <td>35000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>数据分析专家</td>\n","      <td>60000</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>数据分析师</td>\n","      <td>40000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  positionName  salary\n","0         数据分析   37500\n","1         数据建模   15000\n","2         数据分析    3500\n","3         数据分析   45000\n","4         数据分析   30000\n","5         数据分析   50000\n","6         数据分析   30000\n","7      数据建模工程师   35000\n","8       数据分析专家   60000\n","9        数据分析师   40000"]},"execution_count":133,"metadata":{},"output_type":"execute_result"}],"source":["#备注 从数据1中的前10行中读取positionName, salary两列\n","\n","df = pd.read_csv('数据1.csv',encoding='gbk', usecols=['positionName', 'salary'],nrows = 10)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 102.从CSV文件中读取指定数据"]},{"cell_type":"code","execution_count":134,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>学历要求</th>\n","      <th>薪资水平</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>本科</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>硕士</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>本科</td>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>本科</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>不限</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1149</th>\n","      <td>硕士</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1150</th>\n","      <td>本科</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1151</th>\n","      <td>本科</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1152</th>\n","      <td>本科</td>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1153</th>\n","      <td>本科</td>\n","      <td>高</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1154 rows × 2 columns</p>\n","</div>"],"text/plain":["     学历要求 薪资水平\n","0      本科    高\n","1      硕士    高\n","2      本科    低\n","3      本科    高\n","4      不限    高\n","...   ...  ...\n","1149   硕士    高\n","1150   本科    高\n","1151   本科    高\n","1152   本科    高\n","1153   本科    高\n","\n","[1154 rows x 2 columns]"]},"execution_count":134,"metadata":{},"output_type":"execute_result"}],"source":["#备注 从数据2中读取数据并在读取数据时将薪资大于10000的为改为高\n","\n","df = pd.read_csv('数据2.csv',converters={'薪资水平': lambda x: '高' if float(x) > 10000 else '低'} )\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 103.从上一题数据中，对薪资水平列每隔20行进行一次抽样"]},{"cell_type":"code","execution_count":135,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>薪资水平</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>60</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>80</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>120</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>140</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>160</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>180</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>200</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>220</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>240</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>260</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>280</th>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>300</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>320</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>340</th>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>360</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>380</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>400</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>420</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>440</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>460</th>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>480</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>500</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>520</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>540</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>560</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>580</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>600</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>620</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>640</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>660</th>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>680</th>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>700</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>720</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>740</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>760</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>780</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>800</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>820</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>840</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>860</th>\n","      <td>低</td>\n","    </tr>\n","    <tr>\n","      <th>880</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>900</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>920</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>940</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>960</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>980</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1000</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1020</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1040</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1060</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1080</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1100</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1120</th>\n","      <td>高</td>\n","    </tr>\n","    <tr>\n","      <th>1140</th>\n","      <td>高</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     薪资水平\n","0       高\n","20      高\n","40      高\n","60      高\n","80      高\n","100     高\n","120     高\n","140     高\n","160     高\n","180     高\n","200     高\n","220     高\n","240     高\n","260     高\n","280     低\n","300     高\n","320     高\n","340     低\n","360     高\n","380     高\n","400     高\n","420     高\n","440     高\n","460     低\n","480     高\n","500     高\n","520     高\n","540     高\n","560     高\n","580     高\n","600     高\n","620     高\n","640     高\n","660     低\n","680     低\n","700     高\n","720     高\n","740     高\n","760     高\n","780     高\n","800     高\n","820     高\n","840     高\n","860     低\n","880     高\n","900     高\n","920     高\n","940     高\n","960     高\n","980     高\n","1000    高\n","1020    高\n","1040    高\n","1060    高\n","1080    高\n","1100    高\n","1120    高\n","1140    高"]},"execution_count":135,"metadata":{},"output_type":"execute_result"}],"source":["df.iloc[::20, :][['薪资水平']]"]},{"cell_type":"markdown","metadata":{},"source":["### 104.将数据取消使用科学计数法"]},{"cell_type":"code","execution_count":137,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>data</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6.006718e-01</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.152194e-07</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.611162e-13</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3.061740e-01</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.512660e-05</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>4.206163e-01</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.344766e-01</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>4.970202e-07</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>4.707315e-01</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>7.739988e-02</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           data\n","0  6.006718e-01\n","1  1.152194e-07\n","2  1.611162e-13\n","3  3.061740e-01\n","4  1.512660e-05\n","5  4.206163e-01\n","6  1.344766e-01\n","7  4.970202e-07\n","8  4.707315e-01\n","9  7.739988e-02"]},"execution_count":137,"metadata":{},"output_type":"execute_result"}],"source":["#输入\n","df = pd.DataFrame(np.random.random(10)**10, columns=['data'])\n","df"]},{"cell_type":"code","execution_count":138,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>data</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.601</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.306</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.421</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.134</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.471</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.077</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    data\n","0  0.601\n","1  0.000\n","2  0.000\n","3  0.306\n","4  0.000\n","5  0.421\n","6  0.134\n","7  0.000\n","8  0.471\n","9  0.077"]},"execution_count":138,"metadata":{},"output_type":"execute_result"}],"source":["df.round(3)"]},{"cell_type":"markdown","metadata":{},"source":["### 105.将上一题的数据转换为百分数"]},{"cell_type":"code","execution_count":139,"metadata":{},"outputs":[{"data":{"text/html":["<style  type=\"text/css\" >\n","</style><table id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >data</th>    </tr></thead><tbody>\n","                <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row0_col0\" class=\"data row0 col0\" >60.07%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row1_col0\" class=\"data row1 col0\" >0.00%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row2_col0\" class=\"data row2 col0\" >0.00%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row3_col0\" class=\"data row3 col0\" >30.62%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row4_col0\" class=\"data row4 col0\" >0.00%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row5_col0\" class=\"data row5 col0\" >42.06%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row6_col0\" class=\"data row6 col0\" >13.45%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row7_col0\" class=\"data row7 col0\" >0.00%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row8_col0\" class=\"data row8 col0\" >47.07%</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","                        <td id=\"T_9fa891e2_74bf_11ea_b97c_8c8590ab9138row9_col0\" class=\"data row9 col0\" >7.74%</td>\n","            </tr>\n","    </tbody></table>"],"text/plain":["<pandas.io.formats.style.Styler at 0x12b5e1210>"]},"execution_count":139,"metadata":{},"output_type":"execute_result"}],"source":["df.style.format({'data': '{0:.2%}'.format})"]},{"cell_type":"markdown","metadata":{},"source":["### 106.查找上一题数据中第3大值的行号"]},{"cell_type":"code","execution_count":140,"metadata":{},"outputs":[{"data":{"text/plain":["5"]},"execution_count":140,"metadata":{},"output_type":"execute_result"}],"source":["df['data'].argsort()[::-1][7]"]},{"cell_type":"markdown","metadata":{},"source":["### 107.反转df的行"]},{"cell_type":"code","execution_count":141,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>data</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>9</th>\n","      <td>7.739988e-02</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>4.707315e-01</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>4.970202e-07</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.344766e-01</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>4.206163e-01</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.512660e-05</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3.061740e-01</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.611162e-13</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.152194e-07</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>6.006718e-01</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           data\n","9  7.739988e-02\n","8  4.707315e-01\n","7  4.970202e-07\n","6  1.344766e-01\n","5  4.206163e-01\n","4  1.512660e-05\n","3  3.061740e-01\n","2  1.611162e-13\n","1  1.152194e-07\n","0  6.006718e-01"]},"execution_count":141,"metadata":{},"output_type":"execute_result"}],"source":["df.iloc[::-1, :]"]},{"cell_type":"markdown","metadata":{},"source":["### 108.按照多列对数据进行合并"]},{"cell_type":"code","execution_count":142,"metadata":{},"outputs":[],"source":["#输入\n","df1= pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n","'key2': ['K0', 'K1', 'K0', 'K1'],\n","'A': ['A0', 'A1', 'A2', 'A3'],\n","'B': ['B0', 'B1', 'B2', 'B3']})\n","\n","df2= pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n","'key2': ['K0', 'K0', 'K0', 'K0'],\n","'C': ['C0', 'C1', 'C2', 'C3'],\n","'D': ['D0', 'D1', 'D2', 'D3']})"]},{"cell_type":"code","execution_count":143,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>key1</th>\n","      <th>key2</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>K0</td>\n","      <td>K0</td>\n","      <td>A0</td>\n","      <td>B0</td>\n","      <td>C0</td>\n","      <td>D0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>K1</td>\n","      <td>K0</td>\n","      <td>A2</td>\n","      <td>B2</td>\n","      <td>C1</td>\n","      <td>D1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>K1</td>\n","      <td>K0</td>\n","      <td>A2</td>\n","      <td>B2</td>\n","      <td>C2</td>\n","      <td>D2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  key1 key2   A   B   C   D\n","0   K0   K0  A0  B0  C0  D0\n","1   K1   K0  A2  B2  C1  D1\n","2   K1   K0  A2  B2  C2  D2"]},"execution_count":143,"metadata":{},"output_type":"execute_result"}],"source":["pd.merge(df1, df2, on=['key1', 'key2'])"]},{"cell_type":"markdown","metadata":{},"source":["### 109.按照多列对数据进行合并"]},{"cell_type":"code","execution_count":144,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>key1</th>\n","      <th>key2</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>K0</td>\n","      <td>K0</td>\n","      <td>A0</td>\n","      <td>B0</td>\n","      <td>C0</td>\n","      <td>D0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>K0</td>\n","      <td>K1</td>\n","      <td>A1</td>\n","      <td>B1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>K1</td>\n","      <td>K0</td>\n","      <td>A2</td>\n","      <td>B2</td>\n","      <td>C1</td>\n","      <td>D1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>K1</td>\n","      <td>K0</td>\n","      <td>A2</td>\n","      <td>B2</td>\n","      <td>C2</td>\n","      <td>D2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>K2</td>\n","      <td>K1</td>\n","      <td>A3</td>\n","      <td>B3</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  key1 key2   A   B    C    D\n","0   K0   K0  A0  B0   C0   D0\n","1   K0   K1  A1  B1  NaN  NaN\n","2   K1   K0  A2  B2   C1   D1\n","3   K1   K0  A2  B2   C2   D2\n","4   K2   K1  A3  B3  NaN  NaN"]},"execution_count":144,"metadata":{},"output_type":"execute_result"}],"source":["pd.merge(df1, df2, how='left', on=['key1', 'key2'])"]},{"cell_type":"markdown","metadata":{},"source":["### 110.再次读取数据1并显示所有的列"]},{"cell_type":"code","execution_count":145,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>positionId</th>\n","      <th>positionName</th>\n","      <th>companyId</th>\n","      <th>companyLogo</th>\n","      <th>companySize</th>\n","      <th>industryField</th>\n","      <th>financeStage</th>\n","      <th>companyLabelList</th>\n","      <th>firstType</th>\n","      <th>secondType</th>\n","      <th>thirdType</th>\n","      <th>skillLables</th>\n","      <th>positionLables</th>\n","      <th>industryLables</th>\n","      <th>createTime</th>\n","      <th>formatCreateTime</th>\n","      <th>district</th>\n","      <th>businessZones</th>\n","      <th>salary</th>\n","      <th>workYear</th>\n","      <th>jobNature</th>\n","      <th>education</th>\n","      <th>positionAdvantage</th>\n","      <th>imState</th>\n","      <th>lastLogin</th>\n","      <th>publisherId</th>\n","      <th>approve</th>\n","      <th>subwayline</th>\n","      <th>stationname</th>\n","      <th>linestaion</th>\n","      <th>latitude</th>\n","      <th>longitude</th>\n","      <th>hitags</th>\n","      <th>resumeProcessRate</th>\n","      <th>resumeProcessDay</th>\n","      <th>score</th>\n","      <th>newScore</th>\n","      <th>matchScore</th>\n","      <th>matchScoreExplain</th>\n","      <th>query</th>\n","      <th>explain</th>\n","      <th>isSchoolJob</th>\n","      <th>adWord</th>\n","      <th>plus</th>\n","      <th>pcShow</th>\n","      <th>appShow</th>\n","      <th>deliver</th>\n","      <th>gradeDescription</th>\n","      <th>promotionScoreExplain</th>\n","      <th>isHotHire</th>\n","      <th>count</th>\n","      <th>aggregatePositionIds</th>\n","      <th>famousCompany</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6802721</td>\n","      <td>数据分析</td>\n","      <td>475770</td>\n","      <td>i/image2/M01/B7/3E/CgoB5lwPfEaAdn8WAABWQ0Jgl5s...</td>\n","      <td>50-150人</td>\n","      <td>移动互联网,电商</td>\n","      <td>A轮</td>\n","      <td>['绩效奖金', '带薪年假', '定期体检', '弹性工作']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>数据分析</td>\n","      <td>['SQL', '数据库', '数据运营', 'BI']</td>\n","      <td>['电商', '社交', 'SQL', '数据库', '数据运营', 'BI']</td>\n","      <td>['电商', '社交', 'SQL', '数据库', '数据运营', 'BI']</td>\n","      <td>2020/3/16 11:00</td>\n","      <td>11:00发布</td>\n","      <td>余杭区</td>\n","      <td>['仓前']</td>\n","      <td>37500</td>\n","      <td>1-3年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>五险一金、弹性工作、带薪年假、年度体检</td>\n","      <td>today</td>\n","      <td>2020/3/16 11:00</td>\n","      <td>12022406</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.278421</td>\n","      <td>120.005922</td>\n","      <td>NaN</td>\n","      <td>50</td>\n","      <td>1</td>\n","      <td>233</td>\n","      <td>0</td>\n","      <td>15.101875</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>5204912</td>\n","      <td>数据建模</td>\n","      <td>50735</td>\n","      <td>image1/M00/00/85/CgYXBlTUXeeAR0IjAABbroUk-dw97...</td>\n","      <td>150-500人</td>\n","      <td>电商</td>\n","      <td>B轮</td>\n","      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>建模</td>\n","      <td>['算法', '数据架构']</td>\n","      <td>['算法', '数据架构']</td>\n","      <td>[]</td>\n","      <td>2020/3/16 11:08</td>\n","      <td>11:08发布</td>\n","      <td>滨江区</td>\n","      <td>['西兴', '长河']</td>\n","      <td>15000</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>六险一金,定期体检,丰厚年终</td>\n","      <td>disabled</td>\n","      <td>2020/3/16 11:08</td>\n","      <td>5491688</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.188041</td>\n","      <td>120.201179</td>\n","      <td>NaN</td>\n","      <td>23</td>\n","      <td>1</td>\n","      <td>176</td>\n","      <td>0</td>\n","      <td>32.559414</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>6877668</td>\n","      <td>数据分析</td>\n","      <td>100125</td>\n","      <td>image2/M00/0C/57/CgqLKVYcOA2ADcFuAAAE8MukIKA74...</td>\n","      <td>2000人以上</td>\n","      <td>移动互联网,企业服务</td>\n","      <td>上市公司</td>\n","      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>数据分析</td>\n","      <td>['数据库', '数据分析', 'SQL']</td>\n","      <td>['数据库', 'SQL']</td>\n","      <td>[]</td>\n","      <td>2020/3/16 10:33</td>\n","      <td>10:33发布</td>\n","      <td>江干区</td>\n","      <td>['四季青', '钱江新城']</td>\n","      <td>3500</td>\n","      <td>1-3年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>五险一金 周末双休 不加班 节日福利</td>\n","      <td>today</td>\n","      <td>2020/3/16 10:33</td>\n","      <td>5322583</td>\n","      <td>1</td>\n","      <td>4号线</td>\n","      <td>江锦路</td>\n","      <td>4号线_城星路;4号线_市民中心;4号线_江锦路</td>\n","      <td>30.241521</td>\n","      <td>120.212539</td>\n","      <td>NaN</td>\n","      <td>11</td>\n","      <td>4</td>\n","      <td>80</td>\n","      <td>0</td>\n","      <td>14.972357</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>6496141</td>\n","      <td>数据分析</td>\n","      <td>26564</td>\n","      <td>i/image2/M01/F7/3F/CgoB5lyGAQGAZeI-AAAdOqXecnw...</td>\n","      <td>500-2000人</td>\n","      <td>电商</td>\n","      <td>D轮及以上</td>\n","      <td>['生日趴', '每月腐败基金', '每月补贴', '年度旅游']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据分析</td>\n","      <td>[]</td>\n","      <td>['电商']</td>\n","      <td>['电商']</td>\n","      <td>2020/3/16 10:10</td>\n","      <td>10:10发布</td>\n","      <td>江干区</td>\n","      <td>NaN</td>\n","      <td>45000</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>年终奖等</td>\n","      <td>threeDays</td>\n","      <td>2020/3/16 10:10</td>\n","      <td>9814560</td>\n","      <td>1</td>\n","      <td>1号线</td>\n","      <td>文泽路</td>\n","      <td>1号线_文泽路</td>\n","      <td>30.299404</td>\n","      <td>120.350304</td>\n","      <td>NaN</td>\n","      <td>100</td>\n","      <td>1</td>\n","      <td>68</td>\n","      <td>0</td>\n","      <td>12.874153</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>6467417</td>\n","      <td>数据分析</td>\n","      <td>29211</td>\n","      <td>i/image2/M01/77/B8/CgoB5l1WDyGATNP5AAAlY3h88SY...</td>\n","      <td>2000人以上</td>\n","      <td>物流丨运输</td>\n","      <td>上市公司</td>\n","      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>数据分析</td>\n","      <td>['BI', '数据分析', '数据运营']</td>\n","      <td>['BI', '数据运营']</td>\n","      <td>[]</td>\n","      <td>2020/3/16 09:56</td>\n","      <td>09:56发布</td>\n","      <td>余杭区</td>\n","      <td>['仓前']</td>\n","      <td>30000</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>大专</td>\n","      <td>五险一金</td>\n","      <td>disabled</td>\n","      <td>2020/3/16 09:56</td>\n","      <td>6392394</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.282952</td>\n","      <td>120.009765</td>\n","      <td>NaN</td>\n","      <td>20</td>\n","      <td>1</td>\n","      <td>66</td>\n","      <td>0</td>\n","      <td>12.755375</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>6884346</td>\n","      <td>数据分析师</td>\n","      <td>21236</td>\n","      <td>i/image/M00/43/F6/CgqKkVeEh76AUVPoAAA2Bj747wU6...</td>\n","      <td>500-2000人</td>\n","      <td>移动互联网,医疗丨健康</td>\n","      <td>C轮</td>\n","      <td>['技能培训', '年底双薪', '节日礼物', '绩效奖金']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>数据分析</td>\n","      <td>['数据库', '商业', '数据分析', 'SQL']</td>\n","      <td>['医疗健康', '数据库', '商业', '数据分析', 'SQL']</td>\n","      <td>['医疗健康', '数据库', '商业', '数据分析', 'SQL']</td>\n","      <td>2020/3/11 16:45</td>\n","      <td>2020/3/11</td>\n","      <td>萧山区</td>\n","      <td>NaN</td>\n","      <td>25000</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>不限</td>\n","      <td>大牛老板，开放环境，民生行业，龙头公司</td>\n","      <td>threeDays</td>\n","      <td>2020/3/16 09:49</td>\n","      <td>1665167</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.203078</td>\n","      <td>120.247069</td>\n","      <td>NaN</td>\n","      <td>96</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.314259</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>6849100</td>\n","      <td>商业数据分析</td>\n","      <td>72076</td>\n","      <td>i/image2/M01/92/A4/CgotOV2LPUmAR_8dAAB_DlDMiXA...</td>\n","      <td>500-2000人</td>\n","      <td>移动互联网,电商</td>\n","      <td>C轮</td>\n","      <td>['节日礼物', '股票期权', '带薪年假', '年度旅游']</td>\n","      <td>市场|商务类</td>\n","      <td>市场|营销</td>\n","      <td>商业数据分析</td>\n","      <td>['市场', '数据分析', '行业分析', '市场分析']</td>\n","      <td>['电商', '市场', '数据分析', '行业分析', '市场分析']</td>\n","      <td>['电商', '市场', '数据分析', '行业分析', '市场分析']</td>\n","      <td>2020/3/14 17:38</td>\n","      <td>2天前发布</td>\n","      <td>余杭区</td>\n","      <td>NaN</td>\n","      <td>35000</td>\n","      <td>1-3年</td>\n","      <td>全职</td>\n","      <td>硕士</td>\n","      <td>五险一金、带薪休假</td>\n","      <td>threeDays</td>\n","      <td>2020/3/14 17:38</td>\n","      <td>1732416</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.276694</td>\n","      <td>119.990918</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.283276</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>6803432</td>\n","      <td>奔驰·耀出行-BI数据分析专家</td>\n","      <td>751158</td>\n","      <td>i/image3/M01/64/93/Cgq2xl48z2mAeYRoAAD6Qf_Jeq8...</td>\n","      <td>150-500人</td>\n","      <td>移动互联网</td>\n","      <td>不需要融资</td>\n","      <td>[]</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据分析</td>\n","      <td>['MySQL', '数据处理', '数据分析']</td>\n","      <td>['MySQL', '数据处理', '数据分析']</td>\n","      <td>[]</td>\n","      <td>2020/3/14 22:39</td>\n","      <td>2天前发布</td>\n","      <td>滨江区</td>\n","      <td>['西兴']</td>\n","      <td>30000</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>奔驰 吉利 世界500强</td>\n","      <td>threeDays</td>\n","      <td>2020/3/14 22:39</td>\n","      <td>4785643</td>\n","      <td>1</td>\n","      <td>1号线</td>\n","      <td>滨和路</td>\n","      <td>1号线_滨和路;1号线_江陵路;1号线_滨和路;1号线_江陵路</td>\n","      <td>30.208562</td>\n","      <td>120.219225</td>\n","      <td>NaN</td>\n","      <td>63</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.256719</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>6704835</td>\n","      <td>BI数据分析师</td>\n","      <td>52840</td>\n","      <td>i/image2/M00/26/CA/CgoB5lofsguAfk9ZAACoL3r4p24...</td>\n","      <td>2000人以上</td>\n","      <td>电商</td>\n","      <td>上市公司</td>\n","      <td>['技能培训', '年底双薪', '节日礼物', '绩效奖金']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据分析</td>\n","      <td>['SQLServer', '数据分析']</td>\n","      <td>['电商', '新零售', 'SQLServer', '数据分析']</td>\n","      <td>['电商', '新零售', 'SQLServer', '数据分析']</td>\n","      <td>2020/3/9 15:00</td>\n","      <td>2020/3/9</td>\n","      <td>余杭区</td>\n","      <td>['仓前']</td>\n","      <td>20000</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>阿里巴巴；商业智能；</td>\n","      <td>threeDays</td>\n","      <td>2020/3/16 10:15</td>\n","      <td>5846350</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.280177</td>\n","      <td>120.023521</td>\n","      <td>['16薪', '一年调薪2次']</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.281062</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>6728058</td>\n","      <td>数据分析专家-LQ(J181203029)</td>\n","      <td>2474</td>\n","      <td>i/image2/M01/14/4D/CgoB5lyq5fqAAHHzAAAa148hbk8...</td>\n","      <td>2000人以上</td>\n","      <td>汽车丨出行</td>\n","      <td>不需要融资</td>\n","      <td>['弹性工作', '节日礼物', '岗位晋升', '技能培训']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>其他数据分析</td>\n","      <td>[]</td>\n","      <td>['滴滴']</td>\n","      <td>['滴滴']</td>\n","      <td>2020/3/13 18:24</td>\n","      <td>3天前发布</td>\n","      <td>西湖区</td>\n","      <td>NaN</td>\n","      <td>21500</td>\n","      <td>5-10年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>广阔平台诱人福利</td>\n","      <td>disabled</td>\n","      <td>2020/3/13 19:51</td>\n","      <td>6799495</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.290746</td>\n","      <td>120.074315</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.159343</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>105 rows × 53 columns</p>\n","</div>"],"text/plain":["     positionId           positionName  companyId  \\\n","0       6802721                   数据分析     475770   \n","1       5204912                   数据建模      50735   \n","2       6877668                   数据分析     100125   \n","3       6496141                   数据分析      26564   \n","4       6467417                   数据分析      29211   \n","..          ...                    ...        ...   \n","100     6884346                  数据分析师      21236   \n","101     6849100                 商业数据分析      72076   \n","102     6803432        奔驰·耀出行-BI数据分析专家     751158   \n","103     6704835                BI数据分析师      52840   \n","104     6728058  数据分析专家-LQ(J181203029)       2474   \n","\n","                                           companyLogo companySize  \\\n","0    i/image2/M01/B7/3E/CgoB5lwPfEaAdn8WAABWQ0Jgl5s...     50-150人   \n","1    image1/M00/00/85/CgYXBlTUXeeAR0IjAABbroUk-dw97...    150-500人   \n","2    image2/M00/0C/57/CgqLKVYcOA2ADcFuAAAE8MukIKA74...     2000人以上   \n","3    i/image2/M01/F7/3F/CgoB5lyGAQGAZeI-AAAdOqXecnw...   500-2000人   \n","4    i/image2/M01/77/B8/CgoB5l1WDyGATNP5AAAlY3h88SY...     2000人以上   \n","..                                                 ...         ...   \n","100  i/image/M00/43/F6/CgqKkVeEh76AUVPoAAA2Bj747wU6...   500-2000人   \n","101  i/image2/M01/92/A4/CgotOV2LPUmAR_8dAAB_DlDMiXA...   500-2000人   \n","102  i/image3/M01/64/93/Cgq2xl48z2mAeYRoAAD6Qf_Jeq8...    150-500人   \n","103  i/image2/M00/26/CA/CgoB5lofsguAfk9ZAACoL3r4p24...     2000人以上   \n","104  i/image2/M01/14/4D/CgoB5lyq5fqAAHHzAAAa148hbk8...     2000人以上   \n","\n","    industryField financeStage                   companyLabelList  firstType  \\\n","0        移动互联网,电商           A轮   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类   \n","1              电商           B轮   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类   \n","2      移动互联网,企业服务         上市公司   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类   \n","3              电商        D轮及以上  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类   \n","4           物流丨运输         上市公司   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类   \n","..            ...          ...                                ...        ...   \n","100   移动互联网,医疗丨健康           C轮   ['技能培训', '年底双薪', '节日礼物', '绩效奖金']  产品|需求|项目类   \n","101      移动互联网,电商           C轮   ['节日礼物', '股票期权', '带薪年假', '年度旅游']     市场|商务类   \n","102         移动互联网        不需要融资                                 []  开发|测试|运维类   \n","103            电商         上市公司   ['技能培训', '年底双薪', '节日礼物', '绩效奖金']  开发|测试|运维类   \n","104         汽车丨出行        不需要融资   ['弹性工作', '节日礼物', '岗位晋升', '技能培训']  产品|需求|项目类   \n","\n","    secondType thirdType                     skillLables  \\\n","0         数据分析      数据分析    ['SQL', '数据库', '数据运营', 'BI']   \n","1         数据开发        建模                  ['算法', '数据架构']   \n","2         数据分析      数据分析          ['数据库', '数据分析', 'SQL']   \n","3         数据开发      数据分析                              []   \n","4         数据分析      数据分析          ['BI', '数据分析', '数据运营']   \n","..         ...       ...                             ...   \n","100       数据分析      数据分析    ['数据库', '商业', '数据分析', 'SQL']   \n","101      市场|营销    商业数据分析  ['市场', '数据分析', '行业分析', '市场分析']   \n","102       数据开发      数据分析       ['MySQL', '数据处理', '数据分析']   \n","103       数据开发      数据分析           ['SQLServer', '数据分析']   \n","104       数据分析    其他数据分析                              []   \n","\n","                               positionLables  \\\n","0    ['电商', '社交', 'SQL', '数据库', '数据运营', 'BI']   \n","1                              ['算法', '数据架构']   \n","2                              ['数据库', 'SQL']   \n","3                                      ['电商']   \n","4                              ['BI', '数据运营']   \n","..                                        ...   \n","100      ['医疗健康', '数据库', '商业', '数据分析', 'SQL']   \n","101      ['电商', '市场', '数据分析', '行业分析', '市场分析']   \n","102                 ['MySQL', '数据处理', '数据分析']   \n","103        ['电商', '新零售', 'SQLServer', '数据分析']   \n","104                                    ['滴滴']   \n","\n","                               industryLables       createTime  \\\n","0    ['电商', '社交', 'SQL', '数据库', '数据运营', 'BI']  2020/3/16 11:00   \n","1                                          []  2020/3/16 11:08   \n","2                                          []  2020/3/16 10:33   \n","3                                      ['电商']  2020/3/16 10:10   \n","4                                          []  2020/3/16 09:56   \n","..                                        ...              ...   \n","100      ['医疗健康', '数据库', '商业', '数据分析', 'SQL']  2020/3/11 16:45   \n","101      ['电商', '市场', '数据分析', '行业分析', '市场分析']  2020/3/14 17:38   \n","102                                        []  2020/3/14 22:39   \n","103        ['电商', '新零售', 'SQLServer', '数据分析']   2020/3/9 15:00   \n","104                                    ['滴滴']  2020/3/13 18:24   \n","\n","    formatCreateTime district    businessZones  salary workYear jobNature  \\\n","0            11:00发布      余杭区           ['仓前']   37500     1-3年        全职   \n","1            11:08发布      滨江区     ['西兴', '长河']   15000     3-5年        全职   \n","2            10:33发布      江干区  ['四季青', '钱江新城']    3500     1-3年        全职   \n","3            10:10发布      江干区              NaN   45000     3-5年        全职   \n","4            09:56发布      余杭区           ['仓前']   30000     3-5年        全职   \n","..               ...      ...              ...     ...      ...       ...   \n","100        2020/3/11      萧山区              NaN   25000     3-5年        全职   \n","101            2天前发布      余杭区              NaN   35000     1-3年        全职   \n","102            2天前发布      滨江区           ['西兴']   30000     3-5年        全职   \n","103         2020/3/9      余杭区           ['仓前']   20000     3-5年        全职   \n","104            3天前发布      西湖区              NaN   21500    5-10年        全职   \n","\n","    education    positionAdvantage    imState        lastLogin  publisherId  \\\n","0          本科  五险一金、弹性工作、带薪年假、年度体检      today  2020/3/16 11:00     12022406   \n","1          本科       六险一金,定期体检,丰厚年终   disabled  2020/3/16 11:08      5491688   \n","2          本科   五险一金 周末双休 不加班 节日福利      today  2020/3/16 10:33      5322583   \n","3          本科                 年终奖等  threeDays  2020/3/16 10:10      9814560   \n","4          大专                 五险一金   disabled  2020/3/16 09:56      6392394   \n","..        ...                  ...        ...              ...          ...   \n","100        不限  大牛老板，开放环境，民生行业，龙头公司  threeDays  2020/3/16 09:49      1665167   \n","101        硕士            五险一金、带薪休假  threeDays  2020/3/14 17:38      1732416   \n","102        本科         奔驰 吉利 世界500强  threeDays  2020/3/14 22:39      4785643   \n","103        本科           阿里巴巴；商业智能；  threeDays  2020/3/16 10:15      5846350   \n","104        本科             广阔平台诱人福利   disabled  2020/3/13 19:51      6799495   \n","\n","     approve subwayline stationname                       linestaion  \\\n","0          1        NaN         NaN                              NaN   \n","1          1        NaN         NaN                              NaN   \n","2          1        4号线         江锦路         4号线_城星路;4号线_市民中心;4号线_江锦路   \n","3          1        1号线         文泽路                          1号线_文泽路   \n","4          1        NaN         NaN                              NaN   \n","..       ...        ...         ...                              ...   \n","100        1        NaN         NaN                              NaN   \n","101        1        NaN         NaN                              NaN   \n","102        1        1号线         滨和路  1号线_滨和路;1号线_江陵路;1号线_滨和路;1号线_江陵路   \n","103        1        NaN         NaN                              NaN   \n","104        1        NaN         NaN                              NaN   \n","\n","      latitude   longitude             hitags  resumeProcessRate  \\\n","0    30.278421  120.005922                NaN                 50   \n","1    30.188041  120.201179                NaN                 23   \n","2    30.241521  120.212539                NaN                 11   \n","3    30.299404  120.350304                NaN                100   \n","4    30.282952  120.009765                NaN                 20   \n","..         ...         ...                ...                ...   \n","100  30.203078  120.247069                NaN                 96   \n","101  30.276694  119.990918                NaN                  2   \n","102  30.208562  120.219225                NaN                 63   \n","103  30.280177  120.023521  ['16薪', '一年调薪2次']                  0   \n","104  30.290746  120.074315                NaN                  0   \n","\n","     resumeProcessDay  score  newScore  matchScore  matchScoreExplain  query  \\\n","0                   1    233         0   15.101875                NaN    NaN   \n","1                   1    176         0   32.559414                NaN    NaN   \n","2                   4     80         0   14.972357                NaN    NaN   \n","3                   1     68         0   12.874153                NaN    NaN   \n","4                   1     66         0   12.755375                NaN    NaN   \n","..                ...    ...       ...         ...                ...    ...   \n","100                 1      0         0    0.314259                NaN    NaN   \n","101                 3      0         0    0.283276                NaN    NaN   \n","102                 1      0         0    0.256719                NaN    NaN   \n","103                 0      0         0    0.281062                NaN    NaN   \n","104                 0      0         0    0.159343                NaN    NaN   \n","\n","     explain  isSchoolJob  adWord  plus  pcShow  appShow  deliver  \\\n","0        NaN            0       0   NaN       0        0        0   \n","1        NaN            0       0   NaN       0        0        0   \n","2        NaN            0       0   NaN       0        0        0   \n","3        NaN            0       0   NaN       0        0        0   \n","4        NaN            0       0   NaN       0        0        0   \n","..       ...          ...     ...   ...     ...      ...      ...   \n","100      NaN            0       0   NaN       0        0        0   \n","101      NaN            0       0   NaN       0        0        0   \n","102      NaN            0       0   NaN       0        0        0   \n","103      NaN            0       0   NaN       0        0        0   \n","104      NaN            0       0   NaN       0        0        0   \n","\n","     gradeDescription  promotionScoreExplain  isHotHire  count  \\\n","0                 NaN                    NaN          0      0   \n","1                 NaN                    NaN          0      0   \n","2                 NaN                    NaN          0      0   \n","3                 NaN                    NaN          0      0   \n","4                 NaN                    NaN          0      0   \n","..                ...                    ...        ...    ...   \n","100               NaN                    NaN          0      0   \n","101               NaN                    NaN          0      0   \n","102               NaN                    NaN          0      0   \n","103               NaN                    NaN          0      0   \n","104               NaN                    NaN          0      0   \n","\n","    aggregatePositionIds  famousCompany  \n","0                     []          False  \n","1                     []          False  \n","2                     []          False  \n","3                     []           True  \n","4                     []           True  \n","..                   ...            ...  \n","100                   []          False  \n","101                   []          False  \n","102                   []          False  \n","103                   []           True  \n","104                   []           True  \n","\n","[105 rows x 53 columns]"]},"execution_count":145,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv('数据1.csv',encoding='gbk')\n","pd.set_option(\"display.max.columns\", None)\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 111.查找secondType与thirdType值相等的行号"]},{"cell_type":"code","execution_count":146,"metadata":{},"outputs":[{"data":{"text/plain":["(array([  0,   2,   4,   5,   6,  10,  14,  23,  25,  27,  28,  29,  30,\n","         33,  37,  38,  39,  40,  41,  48,  49,  52,  53,  55,  57,  61,\n","         65,  66,  67,  71,  73,  74,  75,  79,  80,  82,  85,  88,  89,\n","         91,  96, 100]),)"]},"execution_count":146,"metadata":{},"output_type":"execute_result"}],"source":["np.where(df.secondType == df.thirdType)"]},{"cell_type":"markdown","metadata":{},"source":["### 112.查找薪资大于平均薪资的第三个数据"]},{"cell_type":"code","execution_count":147,"metadata":{},"outputs":[{"data":{"text/plain":["array([5])"]},"execution_count":147,"metadata":{},"output_type":"execute_result"}],"source":["np.argwhere(df['salary'] > df['salary'].mean())[2]"]},{"cell_type":"markdown","metadata":{},"source":["### 113.将上一题数据的salary列开根号"]},{"cell_type":"code","execution_count":148,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>salary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>193.649167</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>122.474487</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>59.160798</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>212.132034</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>173.205081</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>158.113883</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>187.082869</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>173.205081</td>\n","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>141.421356</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>146.628783</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>105 rows × 1 columns</p>\n","</div>"],"text/plain":["         salary\n","0    193.649167\n","1    122.474487\n","2     59.160798\n","3    212.132034\n","4    173.205081\n","..          ...\n","100  158.113883\n","101  187.082869\n","102  173.205081\n","103  141.421356\n","104  146.628783\n","\n","[105 rows x 1 columns]"]},"execution_count":148,"metadata":{},"output_type":"execute_result"}],"source":["df[['salary']].apply(np.sqrt)"]},{"cell_type":"markdown","metadata":{},"source":["### 114.将上一题数据的linestaion列按_拆分"]},{"cell_type":"code","execution_count":150,"metadata":{},"outputs":[],"source":["df['split'] = df['linestaion'].str.split('_')"]},{"cell_type":"markdown","metadata":{},"source":["### 115.查看上一题数据中一共有多少列"]},{"cell_type":"code","execution_count":151,"metadata":{},"outputs":[{"data":{"text/plain":["54"]},"execution_count":151,"metadata":{},"output_type":"execute_result"}],"source":["df.shape[1]"]},{"cell_type":"markdown","metadata":{},"source":["### 116.提取industryField列以'数据'开头的行"]},{"cell_type":"code","execution_count":153,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>positionId</th>\n","      <th>positionName</th>\n","      <th>companyId</th>\n","      <th>companyLogo</th>\n","      <th>companySize</th>\n","      <th>industryField</th>\n","      <th>financeStage</th>\n","      <th>companyLabelList</th>\n","      <th>firstType</th>\n","      <th>secondType</th>\n","      <th>thirdType</th>\n","      <th>skillLables</th>\n","      <th>positionLables</th>\n","      <th>industryLables</th>\n","      <th>createTime</th>\n","      <th>formatCreateTime</th>\n","      <th>district</th>\n","      <th>businessZones</th>\n","      <th>salary</th>\n","      <th>workYear</th>\n","      <th>jobNature</th>\n","      <th>education</th>\n","      <th>positionAdvantage</th>\n","      <th>imState</th>\n","      <th>lastLogin</th>\n","      <th>publisherId</th>\n","      <th>approve</th>\n","      <th>subwayline</th>\n","      <th>stationname</th>\n","      <th>linestaion</th>\n","      <th>latitude</th>\n","      <th>longitude</th>\n","      <th>hitags</th>\n","      <th>resumeProcessRate</th>\n","      <th>resumeProcessDay</th>\n","      <th>score</th>\n","      <th>newScore</th>\n","      <th>matchScore</th>\n","      <th>matchScoreExplain</th>\n","      <th>query</th>\n","      <th>explain</th>\n","      <th>isSchoolJob</th>\n","      <th>adWord</th>\n","      <th>plus</th>\n","      <th>pcShow</th>\n","      <th>appShow</th>\n","      <th>deliver</th>\n","      <th>gradeDescription</th>\n","      <th>promotionScoreExplain</th>\n","      <th>isHotHire</th>\n","      <th>count</th>\n","      <th>aggregatePositionIds</th>\n","      <th>famousCompany</th>\n","      <th>split</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>8</th>\n","      <td>6458372</td>\n","      <td>数据分析专家</td>\n","      <td>34132</td>\n","      <td>i/image2/M01/F8/DE/CgoB5lyHTJeAP7v9AAFXUt4zJo4...</td>\n","      <td>150-500人</td>\n","      <td>数据服务,广告营销</td>\n","      <td>A轮</td>\n","      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>其他数据分析</td>\n","      <td>['数据分析', '数据运营']</td>\n","      <td>['电商', '广告营销', '数据分析', '数据运营']</td>\n","      <td>['电商', '广告营销', '数据分析', '数据运营']</td>\n","      <td>2020/3/16 10:57</td>\n","      <td>10:57发布</td>\n","      <td>余杭区</td>\n","      <td>NaN</td>\n","      <td>60000</td>\n","      <td>5-10年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>六险一金、境内外旅游、带薪年假、培训发展</td>\n","      <td>today</td>\n","      <td>2020/3/16 09:51</td>\n","      <td>7542546</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.281850</td>\n","      <td>120.015690</td>\n","      <td>NaN</td>\n","      <td>83</td>\n","      <td>1</td>\n","      <td>24</td>\n","      <td>0</td>\n","      <td>1.141952</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>6804629</td>\n","      <td>数据分析师</td>\n","      <td>34132</td>\n","      <td>i/image2/M01/F8/DE/CgoB5lyHTJeAP7v9AAFXUt4zJo4...</td>\n","      <td>150-500人</td>\n","      <td>数据服务,广告营销</td>\n","      <td>A轮</td>\n","      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>数据分析</td>\n","      <td>['数据分析']</td>\n","      <td>['电商', '广告营销', '数据分析']</td>\n","      <td>['电商', '广告营销', '数据分析']</td>\n","      <td>2020/3/16 10:57</td>\n","      <td>10:57发布</td>\n","      <td>余杭区</td>\n","      <td>NaN</td>\n","      <td>30000</td>\n","      <td>不限</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>六险一金 旅游 带薪年假 培训发展 双休</td>\n","      <td>today</td>\n","      <td>2020/3/16 09:51</td>\n","      <td>7542546</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.281850</td>\n","      <td>120.015690</td>\n","      <td>NaN</td>\n","      <td>83</td>\n","      <td>1</td>\n","      <td>17</td>\n","      <td>0</td>\n","      <td>1.161869</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>6804489</td>\n","      <td>资深数据分析师</td>\n","      <td>34132</td>\n","      <td>i/image2/M01/F8/DE/CgoB5lyHTJeAP7v9AAFXUt4zJo4...</td>\n","      <td>150-500人</td>\n","      <td>数据服务,广告营销</td>\n","      <td>A轮</td>\n","      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据分析</td>\n","      <td>['数据分析']</td>\n","      <td>['电商', '数据分析']</td>\n","      <td>['电商', '数据分析']</td>\n","      <td>2020/3/16 10:57</td>\n","      <td>10:57发布</td>\n","      <td>余杭区</td>\n","      <td>NaN</td>\n","      <td>30000</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>六险一金 旅游 带薪年假 培训发展 双休</td>\n","      <td>today</td>\n","      <td>2020/3/16 09:51</td>\n","      <td>7542546</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.281850</td>\n","      <td>120.015690</td>\n","      <td>NaN</td>\n","      <td>83</td>\n","      <td>1</td>\n","      <td>16</td>\n","      <td>0</td>\n","      <td>1.075559</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>6267370</td>\n","      <td>数据分析专家</td>\n","      <td>31544</td>\n","      <td>image1/M00/00/48/CgYXBlTUXOaADKooAABjQoD_n1w50...</td>\n","      <td>150-500人</td>\n","      <td>数据服务</td>\n","      <td>不需要融资</td>\n","      <td>['专业红娘牵线', '节日礼物', '技能培训', '岗位晋升']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据分析</td>\n","      <td>['数据挖掘', '数据分析']</td>\n","      <td>['数据挖掘', '数据分析']</td>\n","      <td>[]</td>\n","      <td>2020/3/16 11:16</td>\n","      <td>11:16发布</td>\n","      <td>滨江区</td>\n","      <td>NaN</td>\n","      <td>20000</td>\n","      <td>5-10年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>五险一金</td>\n","      <td>today</td>\n","      <td>2020/3/16 11:16</td>\n","      <td>466738</td>\n","      <td>1</td>\n","      <td>4号线</td>\n","      <td>中医药大学</td>\n","      <td>4号线_中医药大学;4号线_联庄</td>\n","      <td>30.185480</td>\n","      <td>120.139320</td>\n","      <td>NaN</td>\n","      <td>43</td>\n","      <td>1</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>1.290228</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>[4号线, 中医药大学;4号线, 联庄]</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>6804489</td>\n","      <td>资深数据分析师</td>\n","      <td>34132</td>\n","      <td>i/image2/M01/F8/DE/CgoB5lyHTJeAP7v9AAFXUt4zJo4...</td>\n","      <td>150-500人</td>\n","      <td>数据服务,广告营销</td>\n","      <td>A轮</td>\n","      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据分析</td>\n","      <td>['数据分析']</td>\n","      <td>['电商', '数据分析']</td>\n","      <td>['电商', '数据分析']</td>\n","      <td>2020/3/16 10:57</td>\n","      <td>10:57发布</td>\n","      <td>余杭区</td>\n","      <td>NaN</td>\n","      <td>37500</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>六险一金 旅游 带薪年假 培训发展 双休</td>\n","      <td>today</td>\n","      <td>2020/3/16 09:51</td>\n","      <td>7542546</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.281850</td>\n","      <td>120.015690</td>\n","      <td>NaN</td>\n","      <td>83</td>\n","      <td>1</td>\n","      <td>16</td>\n","      <td>0</td>\n","      <td>1.075712</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>6242470</td>\n","      <td>数据分析师</td>\n","      <td>31544</td>\n","      <td>image1/M00/00/48/CgYXBlTUXOaADKooAABjQoD_n1w50...</td>\n","      <td>150-500人</td>\n","      <td>数据服务</td>\n","      <td>不需要融资</td>\n","      <td>['专业红娘牵线', '节日礼物', '技能培训', '岗位晋升']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>数据分析</td>\n","      <td>['增长黑客', '数据分析', '商业']</td>\n","      <td>['增长黑客', '数据分析', '商业']</td>\n","      <td>[]</td>\n","      <td>2020/3/16 11:16</td>\n","      <td>11:16发布</td>\n","      <td>滨江区</td>\n","      <td>NaN</td>\n","      <td>22500</td>\n","      <td>1-3年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>五险一金</td>\n","      <td>today</td>\n","      <td>2020/3/16 11:16</td>\n","      <td>466738</td>\n","      <td>1</td>\n","      <td>4号线</td>\n","      <td>中医药大学</td>\n","      <td>4号线_中医药大学;4号线_联庄</td>\n","      <td>30.185480</td>\n","      <td>120.139320</td>\n","      <td>NaN</td>\n","      <td>43</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>1.060218</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>[4号线, 中医药大学;4号线, 联庄]</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>6680900</td>\n","      <td>数据分析师 (MJ000250)</td>\n","      <td>114335</td>\n","      <td>i/image2/M00/17/C2/CgoB5ln5lUuAM8oSAADO2Rz54hQ...</td>\n","      <td>150-500人</td>\n","      <td>数据服务</td>\n","      <td>B轮</td>\n","      <td>['股票期权', '弹性工作', '领导好', '五险一金']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>产品经理</td>\n","      <td>数据分析师</td>\n","      <td>['需求分析', '数据']</td>\n","      <td>['企业服务', '大数据', '需求分析', '数据']</td>\n","      <td>['企业服务', '大数据', '需求分析', '数据']</td>\n","      <td>2020/3/16 10:49</td>\n","      <td>10:49发布</td>\n","      <td>西湖区</td>\n","      <td>NaN</td>\n","      <td>27500</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>不限</td>\n","      <td>阿里系创业、云计算生态、餐补、双休</td>\n","      <td>today</td>\n","      <td>2020/3/16 10:49</td>\n","      <td>3859261</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.289482</td>\n","      <td>120.067080</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>0.947202</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>63</th>\n","      <td>6680900</td>\n","      <td>数据分析师 (MJ000250)</td>\n","      <td>114335</td>\n","      <td>i/image2/M00/17/C2/CgoB5ln5lUuAM8oSAADO2Rz54hQ...</td>\n","      <td>150-500人</td>\n","      <td>数据服务</td>\n","      <td>B轮</td>\n","      <td>['股票期权', '弹性工作', '领导好', '五险一金']</td>\n","      <td>产品|需求|项目类</td>\n","      <td>产品经理</td>\n","      <td>数据分析师</td>\n","      <td>['需求分析', '数据']</td>\n","      <td>['企业服务', '大数据', '需求分析', '数据']</td>\n","      <td>['企业服务', '大数据', '需求分析', '数据']</td>\n","      <td>2020/3/16 10:49</td>\n","      <td>10:49发布</td>\n","      <td>西湖区</td>\n","      <td>NaN</td>\n","      <td>27500</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>不限</td>\n","      <td>阿里系创业、云计算生态、餐补、双休</td>\n","      <td>today</td>\n","      <td>2020/3/16 10:49</td>\n","      <td>3859261</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.289482</td>\n","      <td>120.067080</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0.856464</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>78</th>\n","      <td>5683671</td>\n","      <td>数据分析实习生 (MJ000087)</td>\n","      <td>114335</td>\n","      <td>i/image2/M00/17/C2/CgoB5ln5lUuAM8oSAADO2Rz54hQ...</td>\n","      <td>150-500人</td>\n","      <td>数据服务</td>\n","      <td>B轮</td>\n","      <td>['股票期权', '弹性工作', '领导好', '五险一金']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>后端开发</td>\n","      <td>数据采集</td>\n","      <td>['数据挖掘', '机器学习']</td>\n","      <td>['工具软件', '大数据', '数据挖掘', '机器学习']</td>\n","      <td>['工具软件', '大数据', '数据挖掘', '机器学习']</td>\n","      <td>2020/3/16 10:49</td>\n","      <td>10:49发布</td>\n","      <td>西湖区</td>\n","      <td>NaN</td>\n","      <td>26500</td>\n","      <td>应届毕业生</td>\n","      <td>实习</td>\n","      <td>本科</td>\n","      <td>阿里系创业、云计算生态、餐补、双休</td>\n","      <td>today</td>\n","      <td>2020/3/16 10:49</td>\n","      <td>3859261</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.289482</td>\n","      <td>120.067080</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0.898513</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>79</th>\n","      <td>6046866</td>\n","      <td>数据分析师</td>\n","      <td>543802</td>\n","      <td>i/image2/M01/63/3C/CgotOV0ulwOAU8KWAAAsMECc53M...</td>\n","      <td>15-50人</td>\n","      <td>数据服务</td>\n","      <td>不需要融资</td>\n","      <td>[]</td>\n","      <td>产品|需求|项目类</td>\n","      <td>数据分析</td>\n","      <td>数据分析</td>\n","      <td>['可视化', '数据分析', '数据库']</td>\n","      <td>['企业服务', '可视化', '数据分析', '数据库']</td>\n","      <td>['企业服务', '可视化', '数据分析', '数据库']</td>\n","      <td>2020/3/16 10:19</td>\n","      <td>10:19发布</td>\n","      <td>西湖区</td>\n","      <td>['西溪', '文新']</td>\n","      <td>37500</td>\n","      <td>1-3年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>发展潜力，结合业务，项目制</td>\n","      <td>overSevenDays</td>\n","      <td>2020/3/16 10:19</td>\n","      <td>13308385</td>\n","      <td>1</td>\n","      <td>2号线</td>\n","      <td>文新</td>\n","      <td>2号线_文新;2号线_三坝</td>\n","      <td>30.289000</td>\n","      <td>120.088789</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0.902939</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>[2号线, 文新;2号线, 三坝]</td>\n","    </tr>\n","    <tr>\n","      <th>92</th>\n","      <td>6813626</td>\n","      <td>资深数据分析专员</td>\n","      <td>165939</td>\n","      <td>i/image3/M01/65/71/CgpOIF5CFp2ACoo9AAD3IkKwlv8...</td>\n","      <td>150-500人</td>\n","      <td>数据服务</td>\n","      <td>不需要融资</td>\n","      <td>['年底双薪', '带薪年假', '午餐补助', '定期体检']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据分析</td>\n","      <td>['数据分析']</td>\n","      <td>['数据分析']</td>\n","      <td>[]</td>\n","      <td>2020/3/15 12:21</td>\n","      <td>1天前发布</td>\n","      <td>余杭区</td>\n","      <td>NaN</td>\n","      <td>30000</td>\n","      <td>3-5年</td>\n","      <td>全职</td>\n","      <td>不限</td>\n","      <td>阿里旗下、大数据</td>\n","      <td>today</td>\n","      <td>2020/3/15 13:13</td>\n","      <td>8519805</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.281507</td>\n","      <td>120.018621</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.440405</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>94</th>\n","      <td>6818950</td>\n","      <td>资深数据分析师</td>\n","      <td>165939</td>\n","      <td>i/image3/M01/65/71/CgpOIF5CFp2ACoo9AAD3IkKwlv8...</td>\n","      <td>150-500人</td>\n","      <td>数据服务</td>\n","      <td>不需要融资</td>\n","      <td>['年底双薪', '带薪年假', '午餐补助', '定期体检']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据分析</td>\n","      <td>['数据分析']</td>\n","      <td>['数据分析']</td>\n","      <td>[]</td>\n","      <td>2020/3/15 12:21</td>\n","      <td>1天前发布</td>\n","      <td>余杭区</td>\n","      <td>NaN</td>\n","      <td>30000</td>\n","      <td>5-10年</td>\n","      <td>全职</td>\n","      <td>不限</td>\n","      <td>阿里旗下、大数据</td>\n","      <td>today</td>\n","      <td>2020/3/15 13:13</td>\n","      <td>8519805</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.281507</td>\n","      <td>120.018621</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.407209</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>6718750</td>\n","      <td>旅游大数据分析师（杭州）</td>\n","      <td>122019</td>\n","      <td>i/image/M00/1A/4A/CgqKkVb583WABT4BAABM5RuPCmk9...</td>\n","      <td>50-150人</td>\n","      <td>数据服务,企业服务</td>\n","      <td>A轮</td>\n","      <td>['年底双薪', '股票期权', '午餐补助', '定期体检']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>数据开发</td>\n","      <td>数据治理</td>\n","      <td>['数据分析', '数据处理']</td>\n","      <td>['旅游', '大数据', '数据分析', '数据处理']</td>\n","      <td>['旅游', '大数据', '数据分析', '数据处理']</td>\n","      <td>2020/3/12 16:38</td>\n","      <td>2020/3/12</td>\n","      <td>上城区</td>\n","      <td>['湖滨', '吴山']</td>\n","      <td>30000</td>\n","      <td>1-3年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>管理扁平 潜力项目 五险一金 周末双休</td>\n","      <td>sevenDays</td>\n","      <td>2020/3/13 08:48</td>\n","      <td>11347630</td>\n","      <td>1</td>\n","      <td>2号线</td>\n","      <td>中河北路</td>\n","      <td>1号线_定安路;1号线_龙翔桥;1号线_凤起路;1号线_定安路;1号线_龙翔桥;1号线_凤起...</td>\n","      <td>30.254169</td>\n","      <td>120.164651</td>\n","      <td>NaN</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.826756</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>[1号线, 定安路;1号线, 龙翔桥;1号线, 凤起路;1号线, 定安路;1号线, 龙翔桥;...</td>\n","    </tr>\n","    <tr>\n","      <th>98</th>\n","      <td>6655562</td>\n","      <td>数据分析建模工程师</td>\n","      <td>117422215</td>\n","      <td>i/image2/M01/AF/6D/CgotOV3ki4iAOuo3AABbilI8DfA...</td>\n","      <td>50-150人</td>\n","      <td>数据服务,信息安全</td>\n","      <td>A轮</td>\n","      <td>['午餐补助', '带薪年假', '16到18薪', '法定节假日']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>人工智能</td>\n","      <td>机器学习</td>\n","      <td>['机器学习', '建模', '数据挖掘', '算法']</td>\n","      <td>['机器学习', '建模', '数据挖掘', '算法']</td>\n","      <td>[]</td>\n","      <td>2020/3/14 19:00</td>\n","      <td>2天前发布</td>\n","      <td>西湖区</td>\n","      <td>NaN</td>\n","      <td>30000</td>\n","      <td>1-3年</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>海量数据 全链路建模实践 16-18薪</td>\n","      <td>threeDays</td>\n","      <td>2020/3/16 09:30</td>\n","      <td>8810336</td>\n","      <td>1</td>\n","      <td>2号线</td>\n","      <td>丰潭路</td>\n","      <td>2号线_古翠路;2号线_丰潭路</td>\n","      <td>30.291494</td>\n","      <td>120.113955</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.356308</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>[2号线, 古翠路;2号线, 丰潭路]</td>\n","    </tr>\n","    <tr>\n","      <th>99</th>\n","      <td>6677939</td>\n","      <td>数据分析建模工程师（校招）</td>\n","      <td>117422215</td>\n","      <td>i/image2/M01/AF/6D/CgotOV3ki4iAOuo3AABbilI8DfA...</td>\n","      <td>50-150人</td>\n","      <td>数据服务,信息安全</td>\n","      <td>A轮</td>\n","      <td>['午餐补助', '带薪年假', '16到18薪', '法定节假日']</td>\n","      <td>开发|测试|运维类</td>\n","      <td>人工智能</td>\n","      <td>算法工程师</td>\n","      <td>['机器学习', '建模', '算法', '数据挖掘']</td>\n","      <td>['机器学习', '建模', '算法', '数据挖掘']</td>\n","      <td>[]</td>\n","      <td>2020/3/14 19:00</td>\n","      <td>2天前发布</td>\n","      <td>西湖区</td>\n","      <td>NaN</td>\n","      <td>36500</td>\n","      <td>应届毕业生</td>\n","      <td>全职</td>\n","      <td>本科</td>\n","      <td>海量数据 全链路建模实践 16-18薪</td>\n","      <td>threeDays</td>\n","      <td>2020/3/16 09:30</td>\n","      <td>8810336</td>\n","      <td>1</td>\n","      <td>2号线</td>\n","      <td>丰潭路</td>\n","      <td>2号线_古翠路;2号线_丰潭路</td>\n","      <td>30.291494</td>\n","      <td>120.113955</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.338603</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>[]</td>\n","      <td>False</td>\n","      <td>[2号线, 古翠路;2号线, 丰潭路]</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    positionId        positionName  companyId  \\\n","8      6458372              数据分析专家      34132   \n","10     6804629               数据分析师      34132   \n","13     6804489             资深数据分析师      34132   \n","21     6267370              数据分析专家      31544   \n","32     6804489             资深数据分析师      34132   \n","37     6242470               数据分析师      31544   \n","50     6680900    数据分析师 (MJ000250)     114335   \n","63     6680900    数据分析师 (MJ000250)     114335   \n","78     5683671  数据分析实习生 (MJ000087)     114335   \n","79     6046866               数据分析师     543802   \n","92     6813626            资深数据分析专员     165939   \n","94     6818950             资深数据分析师     165939   \n","97     6718750        旅游大数据分析师（杭州）     122019   \n","98     6655562           数据分析建模工程师  117422215   \n","99     6677939       数据分析建模工程师（校招）  117422215   \n","\n","                                          companyLogo companySize  \\\n","8   i/image2/M01/F8/DE/CgoB5lyHTJeAP7v9AAFXUt4zJo4...    150-500人   \n","10  i/image2/M01/F8/DE/CgoB5lyHTJeAP7v9AAFXUt4zJo4...    150-500人   \n","13  i/image2/M01/F8/DE/CgoB5lyHTJeAP7v9AAFXUt4zJo4...    150-500人   \n","21  image1/M00/00/48/CgYXBlTUXOaADKooAABjQoD_n1w50...    150-500人   \n","32  i/image2/M01/F8/DE/CgoB5lyHTJeAP7v9AAFXUt4zJo4...    150-500人   \n","37  image1/M00/00/48/CgYXBlTUXOaADKooAABjQoD_n1w50...    150-500人   \n","50  i/image2/M00/17/C2/CgoB5ln5lUuAM8oSAADO2Rz54hQ...    150-500人   \n","63  i/image2/M00/17/C2/CgoB5ln5lUuAM8oSAADO2Rz54hQ...    150-500人   \n","78  i/image2/M00/17/C2/CgoB5ln5lUuAM8oSAADO2Rz54hQ...    150-500人   \n","79  i/image2/M01/63/3C/CgotOV0ulwOAU8KWAAAsMECc53M...      15-50人   \n","92  i/image3/M01/65/71/CgpOIF5CFp2ACoo9AAD3IkKwlv8...    150-500人   \n","94  i/image3/M01/65/71/CgpOIF5CFp2ACoo9AAD3IkKwlv8...    150-500人   \n","97  i/image/M00/1A/4A/CgqKkVb583WABT4BAABM5RuPCmk9...     50-150人   \n","98  i/image2/M01/AF/6D/CgotOV3ki4iAOuo3AABbilI8DfA...     50-150人   \n","99  i/image2/M01/AF/6D/CgotOV3ki4iAOuo3AABbilI8DfA...     50-150人   \n","\n","   industryField financeStage                     companyLabelList  firstType  \\\n","8      数据服务,广告营销           A轮  ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  产品|需求|项目类   \n","10     数据服务,广告营销           A轮  ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  产品|需求|项目类   \n","13     数据服务,广告营销           A轮  ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  开发|测试|运维类   \n","21          数据服务        不需要融资   ['专业红娘牵线', '节日礼物', '技能培训', '岗位晋升']  开发|测试|运维类   \n","32     数据服务,广告营销           A轮  ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  开发|测试|运维类   \n","37          数据服务        不需要融资   ['专业红娘牵线', '节日礼物', '技能培训', '岗位晋升']  产品|需求|项目类   \n","50          数据服务           B轮      ['股票期权', '弹性工作', '领导好', '五险一金']  产品|需求|项目类   \n","63          数据服务           B轮      ['股票期权', '弹性工作', '领导好', '五险一金']  产品|需求|项目类   \n","78          数据服务           B轮      ['股票期权', '弹性工作', '领导好', '五险一金']  开发|测试|运维类   \n","79          数据服务        不需要融资                                   []  产品|需求|项目类   \n","92          数据服务        不需要融资     ['年底双薪', '带薪年假', '午餐补助', '定期体检']  开发|测试|运维类   \n","94          数据服务        不需要融资     ['年底双薪', '带薪年假', '午餐补助', '定期体检']  开发|测试|运维类   \n","97     数据服务,企业服务           A轮     ['年底双薪', '股票期权', '午餐补助', '定期体检']  开发|测试|运维类   \n","98     数据服务,信息安全           A轮  ['午餐补助', '带薪年假', '16到18薪', '法定节假日']  开发|测试|运维类   \n","99     数据服务,信息安全           A轮  ['午餐补助', '带薪年假', '16到18薪', '法定节假日']  开发|测试|运维类   \n","\n","   secondType thirdType                   skillLables  \\\n","8        数据分析    其他数据分析              ['数据分析', '数据运营']   \n","10       数据分析      数据分析                      ['数据分析']   \n","13       数据开发      数据分析                      ['数据分析']   \n","21       数据开发      数据分析              ['数据挖掘', '数据分析']   \n","32       数据开发      数据分析                      ['数据分析']   \n","37       数据分析      数据分析        ['增长黑客', '数据分析', '商业']   \n","50       产品经理     数据分析师                ['需求分析', '数据']   \n","63       产品经理     数据分析师                ['需求分析', '数据']   \n","78       后端开发      数据采集              ['数据挖掘', '机器学习']   \n","79       数据分析      数据分析        ['可视化', '数据分析', '数据库']   \n","92       数据开发      数据分析                      ['数据分析']   \n","94       数据开发      数据分析                      ['数据分析']   \n","97       数据开发      数据治理              ['数据分析', '数据处理']   \n","98       人工智能      机器学习  ['机器学习', '建模', '数据挖掘', '算法']   \n","99       人工智能     算法工程师  ['机器学习', '建模', '算法', '数据挖掘']   \n","\n","                     positionLables                   industryLables  \\\n","8    ['电商', '广告营销', '数据分析', '数据运营']   ['电商', '广告营销', '数据分析', '数据运营']   \n","10           ['电商', '广告营销', '数据分析']           ['电商', '广告营销', '数据分析']   \n","13                   ['电商', '数据分析']                   ['电商', '数据分析']   \n","21                 ['数据挖掘', '数据分析']                               []   \n","32                   ['电商', '数据分析']                   ['电商', '数据分析']   \n","37           ['增长黑客', '数据分析', '商业']                               []   \n","50    ['企业服务', '大数据', '需求分析', '数据']    ['企业服务', '大数据', '需求分析', '数据']   \n","63    ['企业服务', '大数据', '需求分析', '数据']    ['企业服务', '大数据', '需求分析', '数据']   \n","78  ['工具软件', '大数据', '数据挖掘', '机器学习']  ['工具软件', '大数据', '数据挖掘', '机器学习']   \n","79   ['企业服务', '可视化', '数据分析', '数据库']   ['企业服务', '可视化', '数据分析', '数据库']   \n","92                         ['数据分析']                               []   \n","94                         ['数据分析']                               []   \n","97    ['旅游', '大数据', '数据分析', '数据处理']    ['旅游', '大数据', '数据分析', '数据处理']   \n","98     ['机器学习', '建模', '数据挖掘', '算法']                               []   \n","99     ['机器学习', '建模', '算法', '数据挖掘']                               []   \n","\n","         createTime formatCreateTime district businessZones  salary workYear  \\\n","8   2020/3/16 10:57          10:57发布      余杭区           NaN   60000    5-10年   \n","10  2020/3/16 10:57          10:57发布      余杭区           NaN   30000       不限   \n","13  2020/3/16 10:57          10:57发布      余杭区           NaN   30000     3-5年   \n","21  2020/3/16 11:16          11:16发布      滨江区           NaN   20000    5-10年   \n","32  2020/3/16 10:57          10:57发布      余杭区           NaN   37500     3-5年   \n","37  2020/3/16 11:16          11:16发布      滨江区           NaN   22500     1-3年   \n","50  2020/3/16 10:49          10:49发布      西湖区           NaN   27500     3-5年   \n","63  2020/3/16 10:49          10:49发布      西湖区           NaN   27500     3-5年   \n","78  2020/3/16 10:49          10:49发布      西湖区           NaN   26500    应届毕业生   \n","79  2020/3/16 10:19          10:19发布      西湖区  ['西溪', '文新']   37500     1-3年   \n","92  2020/3/15 12:21            1天前发布      余杭区           NaN   30000     3-5年   \n","94  2020/3/15 12:21            1天前发布      余杭区           NaN   30000    5-10年   \n","97  2020/3/12 16:38        2020/3/12      上城区  ['湖滨', '吴山']   30000     1-3年   \n","98  2020/3/14 19:00            2天前发布      西湖区           NaN   30000     1-3年   \n","99  2020/3/14 19:00            2天前发布      西湖区           NaN   36500    应届毕业生   \n","\n","   jobNature education     positionAdvantage        imState        lastLogin  \\\n","8         全职        本科  六险一金、境内外旅游、带薪年假、培训发展          today  2020/3/16 09:51   \n","10        全职        本科  六险一金 旅游 带薪年假 培训发展 双休          today  2020/3/16 09:51   \n","13        全职        本科  六险一金 旅游 带薪年假 培训发展 双休          today  2020/3/16 09:51   \n","21        全职        本科                  五险一金          today  2020/3/16 11:16   \n","32        全职        本科  六险一金 旅游 带薪年假 培训发展 双休          today  2020/3/16 09:51   \n","37        全职        本科                  五险一金          today  2020/3/16 11:16   \n","50        全职        不限     阿里系创业、云计算生态、餐补、双休          today  2020/3/16 10:49   \n","63        全职        不限     阿里系创业、云计算生态、餐补、双休          today  2020/3/16 10:49   \n","78        实习        本科     阿里系创业、云计算生态、餐补、双休          today  2020/3/16 10:49   \n","79        全职        本科         发展潜力，结合业务，项目制  overSevenDays  2020/3/16 10:19   \n","92        全职        不限              阿里旗下、大数据          today  2020/3/15 13:13   \n","94        全职        不限              阿里旗下、大数据          today  2020/3/15 13:13   \n","97        全职        本科   管理扁平 潜力项目 五险一金 周末双休      sevenDays  2020/3/13 08:48   \n","98        全职        本科   海量数据 全链路建模实践 16-18薪      threeDays  2020/3/16 09:30   \n","99        全职        本科   海量数据 全链路建模实践 16-18薪      threeDays  2020/3/16 09:30   \n","\n","    publisherId  approve subwayline stationname  \\\n","8       7542546        1        NaN         NaN   \n","10      7542546        1        NaN         NaN   \n","13      7542546        1        NaN         NaN   \n","21       466738        1        4号线       中医药大学   \n","32      7542546        1        NaN         NaN   \n","37       466738        1        4号线       中医药大学   \n","50      3859261        1        NaN         NaN   \n","63      3859261        1        NaN         NaN   \n","78      3859261        1        NaN         NaN   \n","79     13308385        1        2号线          文新   \n","92      8519805        1        NaN         NaN   \n","94      8519805        1        NaN         NaN   \n","97     11347630        1        2号线        中河北路   \n","98      8810336        1        2号线         丰潭路   \n","99      8810336        1        2号线         丰潭路   \n","\n","                                           linestaion   latitude   longitude  \\\n","8                                                 NaN  30.281850  120.015690   \n","10                                                NaN  30.281850  120.015690   \n","13                                                NaN  30.281850  120.015690   \n","21                                   4号线_中医药大学;4号线_联庄  30.185480  120.139320   \n","32                                                NaN  30.281850  120.015690   \n","37                                   4号线_中医药大学;4号线_联庄  30.185480  120.139320   \n","50                                                NaN  30.289482  120.067080   \n","63                                                NaN  30.289482  120.067080   \n","78                                                NaN  30.289482  120.067080   \n","79                                      2号线_文新;2号线_三坝  30.289000  120.088789   \n","92                                                NaN  30.281507  120.018621   \n","94                                                NaN  30.281507  120.018621   \n","97  1号线_定安路;1号线_龙翔桥;1号线_凤起路;1号线_定安路;1号线_龙翔桥;1号线_凤起...  30.254169  120.164651   \n","98                                    2号线_古翠路;2号线_丰潭路  30.291494  120.113955   \n","99                                    2号线_古翠路;2号线_丰潭路  30.291494  120.113955   \n","\n","   hitags  resumeProcessRate  resumeProcessDay  score  newScore  matchScore  \\\n","8     NaN                 83                 1     24         0    1.141952   \n","10    NaN                 83                 1     17         0    1.161869   \n","13    NaN                 83                 1     16         0    1.075559   \n","21    NaN                 43                 1      7         0    1.290228   \n","32    NaN                 83                 1     16         0    1.075712   \n","37    NaN                 43                 1      5         0    1.060218   \n","50    NaN                  1                 1      5         0    0.947202   \n","63    NaN                  1                 1      4         0    0.856464   \n","78    NaN                  1                 1      3         0    0.898513   \n","79    NaN                  0                 0      3         0    0.902939   \n","92    NaN                  1                 1      1         0    0.440405   \n","94    NaN                  1                 1      1         0    0.407209   \n","97    NaN                  3                 0      1         0    0.826756   \n","98    NaN                  0                 0      0         0    0.356308   \n","99    NaN                  0                 0      0         0    0.338603   \n","\n","    matchScoreExplain  query  explain  isSchoolJob  adWord  plus  pcShow  \\\n","8                 NaN    NaN      NaN            0       0   NaN       0   \n","10                NaN    NaN      NaN            0       0   NaN       0   \n","13                NaN    NaN      NaN            0       0   NaN       0   \n","21                NaN    NaN      NaN            0       0   NaN       0   \n","32                NaN    NaN      NaN            0       0   NaN       0   \n","37                NaN    NaN      NaN            0       0   NaN       0   \n","50                NaN    NaN      NaN            0       0   NaN       0   \n","63                NaN    NaN      NaN            0       0   NaN       0   \n","78                NaN    NaN      NaN            1       0   NaN       0   \n","79                NaN    NaN      NaN            0       0   NaN       0   \n","92                NaN    NaN      NaN            0       0   NaN       0   \n","94                NaN    NaN      NaN            0       0   NaN       0   \n","97                NaN    NaN      NaN            0       0   NaN       0   \n","98                NaN    NaN      NaN            0       0   NaN       0   \n","99                NaN    NaN      NaN            1       0   NaN       0   \n","\n","    appShow  deliver  gradeDescription  promotionScoreExplain  isHotHire  \\\n","8         0        0               NaN                    NaN          0   \n","10        0        0               NaN                    NaN          0   \n","13        0        0               NaN                    NaN          0   \n","21        0        0               NaN                    NaN          0   \n","32        0        0               NaN                    NaN          0   \n","37        0        0               NaN                    NaN          0   \n","50        0        0               NaN                    NaN          0   \n","63        0        0               NaN                    NaN          0   \n","78        0        0               NaN                    NaN          0   \n","79        0        0               NaN                    NaN          0   \n","92        0        0               NaN                    NaN          0   \n","94        0        0               NaN                    NaN          0   \n","97        0        0               NaN                    NaN          0   \n","98        0        0               NaN                    NaN          0   \n","99        0        0               NaN                    NaN          0   \n","\n","    count aggregatePositionIds  famousCompany  \\\n","8       0                   []          False   \n","10      0                   []          False   \n","13      0                   []          False   \n","21      0                   []          False   \n","32      0                   []          False   \n","37      0                   []          False   \n","50      0                   []          False   \n","63      0                   []          False   \n","78      0                   []          False   \n","79      0                   []          False   \n","92      0                   []          False   \n","94      0                   []          False   \n","97      0                   []          False   \n","98      0                   []          False   \n","99      0                   []          False   \n","\n","                                                split  \n","8                                                 NaN  \n","10                                                NaN  \n","13                                                NaN  \n","21                               [4号线, 中医药大学;4号线, 联庄]  \n","32                                                NaN  \n","37                               [4号线, 中医药大学;4号线, 联庄]  \n","50                                                NaN  \n","63                                                NaN  \n","78                                                NaN  \n","79                                  [2号线, 文新;2号线, 三坝]  \n","92                                                NaN  \n","94                                                NaN  \n","97  [1号线, 定安路;1号线, 龙翔桥;1号线, 凤起路;1号线, 定安路;1号线, 龙翔桥;...  \n","98                                [2号线, 古翠路;2号线, 丰潭路]  \n","99                                [2号线, 古翠路;2号线, 丰潭路]  "]},"execution_count":153,"metadata":{},"output_type":"execute_result"}],"source":["df[df['industryField'].str.startswith('数据')]"]},{"cell_type":"markdown","metadata":{},"source":["### 117.按列制作数据透视表"]},{"cell_type":"code","execution_count":155,"metadata":{"scrolled":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>salary</th>\n","      <th>score</th>\n","    </tr>\n","    <tr>\n","      <th>positionId</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5203054</th>\n","      <td>30000</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>5204912</th>\n","      <td>15000</td>\n","      <td>176.0</td>\n","    </tr>\n","    <tr>\n","      <th>5269002</th>\n","      <td>37500</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>5453691</th>\n","      <td>30000</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>5519962</th>\n","      <td>37500</td>\n","      <td>14.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>6882983</th>\n","      <td>27500</td>\n","      <td>15.0</td>\n","    </tr>\n","    <tr>\n","      <th>6884346</th>\n","      <td>25000</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>6886661</th>\n","      <td>37500</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>6888169</th>\n","      <td>42500</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>6896403</th>\n","      <td>30000</td>\n","      <td>3.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>95 rows × 2 columns</p>\n","</div>"],"text/plain":["            salary  score\n","positionId               \n","5203054      30000    4.0\n","5204912      15000  176.0\n","5269002      37500    1.0\n","5453691      30000    4.0\n","5519962      37500   14.0\n","...            ...    ...\n","6882983      27500   15.0\n","6884346      25000    0.0\n","6886661      37500    5.0\n","6888169      42500    1.0\n","6896403      30000    3.0\n","\n","[95 rows x 2 columns]"]},"execution_count":155,"metadata":{},"output_type":"execute_result"}],"source":["pd.pivot_table(df,values=[\"salary\",\"score\"],index=\"positionId\")"]},{"cell_type":"markdown","metadata":{},"source":["### 118.同时对salary、score两列进行计算"]},{"cell_type":"code","execution_count":156,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>salary</th>\n","      <th>score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>sum</th>\n","      <td>3.331000e+06</td>\n","      <td>1335.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.172381e+04</td>\n","      <td>12.714286</td>\n","    </tr>\n","    <tr>\n","      <th>amin</th>\n","      <td>3.500000e+03</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            salary        score\n","sum   3.331000e+06  1335.000000\n","mean  3.172381e+04    12.714286\n","amin  3.500000e+03     0.000000"]},"execution_count":156,"metadata":{},"output_type":"execute_result"}],"source":["df[[\"salary\",\"score\"]].agg([np.sum,np.mean,np.min])"]},{"cell_type":"markdown","metadata":{},"source":["### 119.对salary求平均，对score列求和"]},{"cell_type":"code","execution_count":157,"metadata":{},"outputs":[{"data":{"text/plain":["salary    3.331000e+06\n","score     1.271429e+01\n","dtype: float64"]},"execution_count":157,"metadata":{},"output_type":"execute_result"}],"source":["df.agg({\"salary\":np.sum,\"score\":np.mean})"]},{"cell_type":"markdown","metadata":{},"source":["### 120.计算并提取平均薪资最高的区"]},{"cell_type":"code","execution_count":158,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>salary</th>\n","    </tr>\n","    <tr>\n","      <th>district</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>萧山区</th>\n","      <td>36250.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           salary\n","district         \n","萧山区       36250.0"]},"execution_count":158,"metadata":{},"output_type":"execute_result"}],"source":["df[['district','salary']].groupby(by='district').mean().sort_values('salary',ascending=False).head(1)"]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python","nbconvert_exporter":"python","file_extension":".py","version":"3.5.2","pygments_lexer":"ipython3"}},"nbformat":4,"nbformat_minor":2}